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-1821, 1820
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-1822, 1821
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-1823, 1822
-1822, 1823
-1824, 1823
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-1828, 1827
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-1829, 1828
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-1830, 1825
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-1831, 1830
-1830, 1831
-1831, 1822
-1822, 1831
-1832, 1831
-1831, 1832
-1833, 1832
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-1838, 1837
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-1839, 1838
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-1839, 1834
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-1840, 1826
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-1844, 1843
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-1852, 1847
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-1856, 1851
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-1857, 1858
-1859, 1857
-1857, 1859
-1860, 1853
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-1860, 1862
-1863, 1846
-1846, 1863
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-1863, 1865
-1866, 1844
-1844, 1866
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-1866, 1868
-1870, 1869
-1869, 1870
-1871, 1870
-1870, 1871
-1872, 1871
-1871, 1872
-1873, 1872
-1872, 1873
-1874, 1873
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-1875, 1874
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-1875, 1876
-1877, 1876
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-1878, 1877
-1877, 1878
-1879, 1878
-1878, 1879
-1879, 1874
-1874, 1879
-1880, 1879
-1879, 1880
-1880, 1871
-1871, 1880
-1881, 1880
-1880, 1881
-1882, 1881
-1881, 1882
-1882, 1869
-1869, 1882
-1883, 1881
-1881, 1883
-1884, 1883
-1883, 1884
-1884, 1878
-1878, 1884
-1885, 1876
-1876, 1885
-1886, 1885
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-1889, 1888
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-1898, 1893
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-1898, 1899
-1899, 1890
-1890, 1899
-1900, 1899
-1899, 1900
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-1900, 1901
-1901, 1888
-1888, 1901
-1902, 1900
-1900, 1902
-1903, 1902
-1902, 1903
-1903, 1897
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-1906, 1905
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-1902, 1907
-1908, 1896
-1896, 1908
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-1911, 1912
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-1920, 1921
-1921, 1916
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-1922, 1921
-1921, 1922
-1922, 1913
-1913, 1922
-1923, 1922
-1922, 1923
-1924, 1923
-1923, 1924
-1924, 1911
-1911, 1924
-1925, 1915
-1915, 1925
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-1927, 1925
-1925, 1927
-1929, 1928
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-1930, 1929
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-1932, 1933
-1934, 1933
-1933, 1934
-1935, 1934
-1934, 1935
-1936, 1935
-1935, 1936
-1937, 1936
-1936, 1937
-1938, 1937
-1937, 1938
-1938, 1933
-1933, 1938
-1939, 1938
-1938, 1939
-1939, 1930
-1930, 1939
-1940, 1939
-1939, 1940
-1941, 1940
-1940, 1941
-1941, 1928
-1928, 1941
-1942, 1940
-1940, 1942
-1943, 1942
-1942, 1943
-1943, 1937
-1937, 1943
-1944, 1943
-1943, 1944
-1945, 1936
-1936, 1945
-1946, 1945
-1945, 1946
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-1945, 1947
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-1948, 1949
-1950, 1949
-1949, 1950
-1951, 1950
-1950, 1951
-1952, 1951
-1951, 1952
-1953, 1952
-1952, 1953
-1953, 1948
-1948, 1953
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-1952, 1954
-1955, 1954
-1954, 1955
-1956, 1955
-1955, 1956
-1957, 1956
-1956, 1957
-1958, 1957
-1957, 1958
-1959, 1958
-1958, 1959
-1959, 1954
-1954, 1959
-1960, 1959
-1959, 1960
-1960, 1951
-1951, 1960
-1961, 1960
-1960, 1961
-1962, 1961
-1961, 1962
-1963, 1962
-1962, 1963
-1963, 1958
-1958, 1963
-1964, 1963
-1963, 1964
-1965, 1964
-1964, 1965
-1966, 1964
-1964, 1966
-1967, 1962
-1962, 1967
-1968, 1967
-1967, 1968
-1969, 1967
-1967, 1969
-1970, 1956
-1956, 1970
-1971, 1970
-1970, 1971
-1972, 1970
-1970, 1972
-1974, 1973
-1973, 1974
-1975, 1974
-1974, 1975
-1976, 1975
-1975, 1976
-1977, 1976
-1976, 1977
-1978, 1977
-1977, 1978
-1978, 1973
-1973, 1978
-1979, 1977
-1977, 1979
-1980, 1979
-1979, 1980
-1981, 1980
-1980, 1981
-1982, 1981
-1981, 1982
-1983, 1982
-1982, 1983
-1984, 1983
-1983, 1984
-1984, 1979
-1979, 1984
-1985, 1984
-1984, 1985
-1985, 1976
-1976, 1985
-1986, 1985
-1985, 1986
-1987, 1986
-1986, 1987
-1988, 1987
-1987, 1988
-1988, 1983
-1983, 1988
-1989, 1988
-1988, 1989
-1990, 1989
-1989, 1990
-1991, 1989
-1989, 1991
-1993, 1992
-1992, 1993
-1994, 1993
-1993, 1994
-1995, 1994
-1994, 1995
-1996, 1995
-1995, 1996
-1997, 1996
-1996, 1997
-1998, 1997
-1997, 1998
-1999, 1998
-1998, 1999
-2000, 1999
-1999, 2000
-2001, 2000
-2000, 2001
-2002, 2001
-2001, 2002
-2002, 1997
-1997, 2002
-2003, 2002
-2002, 2003
-2003, 1994
-1994, 2003
-2004, 2003
-2003, 2004
-2005, 2004
-2004, 2005
-2005, 1992
-1992, 2005
-2006, 2005
-2005, 2006
-2007, 2006
-2006, 2007
-2008, 2007
-2007, 2008
-2009, 2008
-2008, 2009
-2009, 2004
-2004, 2009
-2010, 2009
-2009, 2010
-2010, 2001
-2001, 2010
-2011, 2010
-2010, 2011
-2012, 2011
-2011, 2012
-2013, 2012
-2012, 2013
-2013, 2008
-2008, 2013
-2014, 2013
-2013, 2014
-2015, 2014
-2014, 2015
-2016, 2014
-2014, 2016
-2017, 1998
-1998, 2017
-2018, 2017
-2017, 2018
-2019, 2017
-2017, 2019
-2021, 2020
-2020, 2021
-2022, 2021
-2021, 2022
-2023, 2022
-2022, 2023
-2024, 2023
-2023, 2024
-2025, 2024
-2024, 2025
-2025, 2020
-2020, 2025
-2026, 2024
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-2027, 2026
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-2028, 2026
-2026, 2028
-2029, 2023
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-2032, 2031
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-2033, 2032
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-2033, 2034
-2034, 2029
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-2035, 2034
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-2035, 2036
-2037, 2035
-2035, 2037
-2038, 2032
-2032, 2038
-2039, 2038
-2038, 2039
-2040, 2038
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diff --git a/data/TUDataset/MUTAG/raw/MUTAG_edge_labels.txt b/data/TUDataset/MUTAG/raw/MUTAG_edge_labels.txt
deleted file mode 100644
index 66a74dc..0000000
--- a/data/TUDataset/MUTAG/raw/MUTAG_edge_labels.txt
+++ /dev/null
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diff --git a/data/TUDataset/MUTAG/raw/MUTAG_graph_indicator.txt b/data/TUDataset/MUTAG/raw/MUTAG_graph_indicator.txt
deleted file mode 100644
index 74f6d67..0000000
--- a/data/TUDataset/MUTAG/raw/MUTAG_graph_indicator.txt
+++ /dev/null
@@ -1,3371 +0,0 @@
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diff --git a/data/TUDataset/MUTAG/raw/MUTAG_graph_labels.txt b/data/TUDataset/MUTAG/raw/MUTAG_graph_labels.txt
deleted file mode 100644
index cd9a9c8..0000000
--- a/data/TUDataset/MUTAG/raw/MUTAG_graph_labels.txt
+++ /dev/null
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diff --git a/data/TUDataset/MUTAG/raw/MUTAG_node_labels.txt b/data/TUDataset/MUTAG/raw/MUTAG_node_labels.txt
deleted file mode 100644
index cc11ed9..0000000
--- a/data/TUDataset/MUTAG/raw/MUTAG_node_labels.txt
+++ /dev/null
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diff --git a/data/TUDataset/MUTAG/raw/README.txt b/data/TUDataset/MUTAG/raw/README.txt
deleted file mode 100644
index 7c1cafa..0000000
--- a/data/TUDataset/MUTAG/raw/README.txt
+++ /dev/null
@@ -1,85 +0,0 @@
-README for dataset MUTAG
-
-
-=== Usage ===
-
-This folder contains the following comma separated text files 
-(replace DS by the name of the dataset):
-
-n = total number of nodes
-m = total number of edges
-N = number of graphs
-
-(1) 	DS_A.txt (m lines) 
-	sparse (block diagonal) adjacency matrix for all graphs,
-	each line corresponds to (row, col) resp. (node_id, node_id)
-
-(2) 	DS_graph_indicator.txt (n lines)
-	column vector of graph identifiers for all nodes of all graphs,
-	the value in the i-th line is the graph_id of the node with node_id i
-
-(3) 	DS_graph_labels.txt (N lines) 
-	class labels for all graphs in the dataset,
-	the value in the i-th line is the class label of the graph with graph_id i
-
-(4) 	DS_node_labels.txt (n lines)
-	column vector of node labels,
-	the value in the i-th line corresponds to the node with node_id i
-
-There are OPTIONAL files if the respective information is available:
-
-(5) 	DS_edge_labels.txt (m lines; same size as DS_A_sparse.txt)
-	labels for the edges in DD_A_sparse.txt 
-
-(6) 	DS_edge_attributes.txt (m lines; same size as DS_A.txt)
-	attributes for the edges in DS_A.txt 
-
-(7) 	DS_node_attributes.txt (n lines) 
-	matrix of node attributes,
-	the comma seperated values in the i-th line is the attribute vector of the node with node_id i
-
-(8) 	DS_graph_attributes.txt (N lines) 
-	regression values for all graphs in the dataset,
-	the value in the i-th line is the attribute of the graph with graph_id i
-
-
-=== Description of the dataset === 
-
-The MUTAG dataset consists of 188 chemical compounds divided into two 
-classes according to their mutagenic effect on a bacterium. 
-
-The chemical data was obtained form http://cdb.ics.uci.edu and converted 
-to graphs, where vertices represent atoms and edges represent chemical 
-bonds. Explicit hydrogen atoms have been removed and vertices are labeled
-by atom type and edges by bond type (single, double, triple or aromatic).
-Chemical data was processed using the Chemistry Development Kit (v1.4).
-
-Node labels:
-
-  0  C
-  1  N
-  2  O
-  3  F
-  4  I
-  5  Cl
-  6  Br
-
-Edge labels:
-
-  0  aromatic
-  1  single
-  2  double
-  3  triple
-
-
-=== Previous Use of the Dataset ===
-
-Kriege, N., Mutzel, P.: Subgraph matching kernels for attributed graphs. In: Proceedings
-of the 29th International Conference on Machine Learning (ICML-2012) (2012).
-
-
-=== References ===
-
-Debnath, A.K., Lopez de Compadre, R.L., Debnath, G., Shusterman, A.J., and Hansch, C.
-Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds.
-Correlation with molecular orbital energies and hydrophobicity. J. Med. Chem. 34(2):786-797 (1991).
diff --git a/src/data_process.py b/src/data_process.py
index 92bbd65..6af1599 100644
--- a/src/data_process.py
+++ b/src/data_process.py
@@ -1,19 +1,25 @@
 # setting of data generation
 
-import torch
+import pickle as pkl
 import random
 import sys
-import pickle as pkl
+
+import networkx as nx
 import numpy as np
 import scipy.sparse as sp
-import networkx as nx
+import torch
 import torch_geometric
 import torch_sparse
 
 
-def generate_data(number_of_nodes: int, class_num: int, link_inclass_prob: float, link_outclass_prob: float) -> tuple:
-    '''
-    This function generates a synthetic graph dataset returns components of the graph dataset - features, 
+def generate_data(
+    number_of_nodes: int,
+    class_num: int,
+    link_inclass_prob: float,
+    link_outclass_prob: float,
+) -> tuple:
+    """
+    This function generates a synthetic graph dataset returns components of the graph dataset - features,
     adjacency matrix, labels, and indices for training, validation, and testing.
 
     Args:
@@ -31,62 +37,60 @@ def generate_data(number_of_nodes: int, class_num: int, link_inclass_prob: float
     idx_test: (torch.LongTensor) - Indices of nodes used for test dataset
 
     Notes:
-    '''
-    
-    
-    adj=torch.zeros(number_of_nodes,number_of_nodes) #n*n adj matrix
+    """
 
-    labels=torch.randint(0,class_num,(number_of_nodes,)) #assign random label with equal probability
-    labels=labels.to(dtype=torch.long)
-    #label_node, speed up the generation of edges
-    label_node_dict=dict()
+    adj = torch.zeros(number_of_nodes, number_of_nodes)  # n*n adj matrix
 
-    #Create an empty dictionary for the labels
+    labels = torch.randint(
+        0, class_num, (number_of_nodes,)
+    )  # assign random label with equal probability
+    labels = labels.to(dtype=torch.long)
+    # label_node, speed up the generation of edges
+    label_node_dict: dict[int, list[int]] = dict()
+
+    # Create an empty dictionary for the labels
     for j in range(class_num):
-            label_node_dict[j]=[]
+        label_node_dict[j] = []
 
-    #Populating the above dictionary - for each label with a list of node indices having that label
+    # Populating the above dictionary - for each label with a list of node indices having that label
     for i in range(len(labels)):
-        label_node_dict[int(labels[i])]+=[int(i)]
-
+        label_node_dict[int(labels[i])] += [int(i)]
 
-    #generate graph
+    # generate graph
     for node_id in range(number_of_nodes):
-                j=labels[node_id]
-                for l in label_node_dict:
-                    if l==j: #same class 
-                        for z in label_node_dict[l]:  #z>node_id,  symmetrix matrix, no repeat
-                            if z>node_id and random.random()<link_inclass_prob:
-                                adj[node_id,z]= 1
-                                adj[z,node_id]= 1
-                    else: #different class
-                        for z in label_node_dict[l]:
-                            if z>node_id and random.random()<link_outclass_prob:
-                                adj[node_id,z]= 1
-                                adj[z,node_id]= 1
-                              
-    adj = torch_sparse.tensor.SparseTensor.from_dense(adj.float())
+        j = labels[node_id]
+        for l in label_node_dict:
+            if l == j:  # same class
+                for z in label_node_dict[l]:  # z>node_id,  symmetric matrix, no repeat
+                    if z > node_id and random.random() < link_inclass_prob:
+                        adj[node_id, z] = 1
+                        adj[z, node_id] = 1
+            else:  # different class
+                for z in label_node_dict[l]:
+                    if z > node_id and random.random() < link_outclass_prob:
+                        adj[node_id, z] = 1
+                        adj[z, node_id] = 1
 
-    #generate feature use eye matrix
-    features=torch.eye(number_of_nodes,number_of_nodes)
+    adj = torch_sparse.tensor.SparseTensor.from_dense(adj.float())
 
-    #seprate train,val,test
-    idx_train = torch.LongTensor(range(number_of_nodes//5))
-    idx_val = torch.LongTensor(range(number_of_nodes//5, number_of_nodes//2))
-    idx_test = torch.LongTensor(range(number_of_nodes//2, number_of_nodes))
+    # generate feature use eye matrix
+    features = torch.eye(number_of_nodes, number_of_nodes)
 
+    # separate train,val,test
+    idx_train = torch.LongTensor(range(number_of_nodes // 5))
+    idx_val = torch.LongTensor(range(number_of_nodes // 5, number_of_nodes // 2))
+    idx_test = torch.LongTensor(range(number_of_nodes // 2, number_of_nodes))
 
     return features.float(), adj, labels, idx_train, idx_val, idx_test
 
 
-
 def parse_index_file(filename: str) -> list:
     """
     This function reads and parses an index file
 
-    Args: 
+    Args:
     filename: (str) - name or path of the file to parse
-    
+
     Return:
     index: (list) - list of integers, each integer in the list represents int of the lines lines of the input file.
     """
@@ -95,39 +99,37 @@ def parse_index_file(filename: str) -> list:
         index.append(int(line.strip()))
     return index
 
-    
 
 def normalize(mx: sp.csc_matrix) -> sp.csr_matrix:
     """
     This function is to row-normalize sparse matrix for efficient computation of the graph
-    
+
     Argument:
-    mx: (sparse matrix) - Input sparse matrix to row-normalize. 
+    mx: (sparse matrix) - Input sparse matrix to row-normalize.
 
     Return:
     mx: (sparse matrix) - Returns the row-normalized sparse matrix.
 
     Note:
-    Row-normalizing is usually done in graph algorithms to enable equal node contributions regardless of the node's degree 
+    Row-normalizing is usually done in graph algorithms to enable equal node contributions regardless of the node's degree
     and to stabilize, ease numerical computations
     """
     rowsum = np.array(mx.sum(1))
     r_inv = np.power(rowsum, -1).flatten()
-    r_inv[np.isinf(r_inv)] = 0.
+    r_inv[np.isinf(r_inv)] = 0.0
     r_mat_inv = sp.diags(r_inv)
     mx = r_mat_inv.dot(mx)
     return mx
 
 
-
 def load_data(dataset_str: str) -> tuple:
-    '''
+    """
     This function loads input data from gcn/data directory
 
     Argument:
     dataset_str: Dataset name
 
-    Return: 
+    Return:
     All data input files loaded (as well as the training/test data).
 
     Note:
@@ -143,31 +145,35 @@ def load_data(dataset_str: str) -> tuple:
     ind.dataset_str.test.index => the indices of test instances in graph, for the inductive setting as list object.
 
     All objects above must be saved using python pickle module.
-    '''
+    """
 
-    if dataset_str in ['cora', 'citeseer', 'pubmed']:
-        names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'graph']
+    if dataset_str in ["cora", "citeseer", "pubmed"]:
+        names = ["x", "y", "tx", "ty", "allx", "ally", "graph"]
         objects = []
         for i in range(len(names)):
-            with open("data/ind.{}.{}".format(dataset_str, names[i]), 'rb') as f:
+            with open("data/ind.{}.{}".format(dataset_str, names[i]), "rb") as f:
                 if sys.version_info > (3, 0):
-                    objects.append(pkl.load(f, encoding='latin1'))
+                    objects.append(pkl.load(f, encoding="latin1"))
                 else:
                     objects.append(pkl.load(f))
 
         x, y, tx, ty, allx, ally, graph = tuple(objects)
-        test_idx_reorder = parse_index_file("data/ind.{}.test.index".format(dataset_str))
+        test_idx_reorder = parse_index_file(
+            "data/ind.{}.test.index".format(dataset_str)
+        )
         test_idx_range = np.sort(test_idx_reorder)
 
-        if dataset_str == 'citeseer':
+        if dataset_str == "citeseer":
             # Fix citeseer dataset (there are some isolated nodes in the graph)
             # Find isolated nodes, add them as zero-vecs into the right position
-            test_idx_range_full = range(min(test_idx_reorder), max(test_idx_reorder)+1)
+            test_idx_range_full = range(
+                min(test_idx_reorder), max(test_idx_reorder) + 1
+            )
             tx_extended = sp.lil_matrix((len(test_idx_range_full), x.shape[1]))
-            tx_extended[test_idx_range-min(test_idx_range), :] = tx
+            tx_extended[test_idx_range - min(test_idx_range), :] = tx
             tx = tx_extended
             ty_extended = np.zeros((len(test_idx_range_full), y.shape[1]))
-            ty_extended[test_idx_range-min(test_idx_range), :] = ty
+            ty_extended[test_idx_range - min(test_idx_range), :] = ty
             ty = ty_extended
 
         features = sp.vstack((allx, tx)).tolil()
@@ -177,72 +183,68 @@ def load_data(dataset_str: str) -> tuple:
         labels = np.vstack((ally, ty))
         labels[test_idx_reorder, :] = labels[test_idx_range, :]
 
-        number_of_nodes=adj.shape[0]
+        idx_test = torch.LongTensor(test_idx_range.tolist())
+        idx_train = torch.LongTensor(range(len(y)))
+        idx_val = torch.LongTensor(range(len(y), len(y) + 500))
 
+        # features = normalize(features)
+        # adj = normalize(adj)    # no normalize adj here, normalize it in the training process
 
-        idx_test = test_idx_range.tolist()
-        idx_train = range(len(y))
-        idx_val = range(len(y), len(y)+500)
-
-        idx_train = torch.LongTensor(idx_train)
-        idx_val = torch.LongTensor(idx_val)
-        idx_test = torch.LongTensor(idx_test)
-
-        #features = normalize(features) #cannot converge if use SGD, why??????????
-        #adj = normalize(adj)    # no normalize adj here, normalize it in the training process
-
-
-        features=torch.tensor(features.toarray()).float()
+        features = torch.tensor(features.toarray()).float()
         adj = torch.tensor(adj.toarray()).float()
         adj = torch_sparse.tensor.SparseTensor.from_dense(adj)
-        labels=torch.tensor(labels)
-        labels=torch.argmax(labels,dim=1)
-
-    elif dataset_str in ['ogbn-arxiv', 'ogbn-products', 'ogbn-mag', 'ogbn-papers100M']: #'ogbn-mag' is heteregeneous
+        labels = torch.tensor(labels)
+        labels = torch.argmax(labels, dim=1)
+
+    elif dataset_str in [
+        "ogbn-arxiv",
+        "ogbn-products",
+        "ogbn-mag",
+        "ogbn-papers100M",
+    ]:  #'ogbn-mag' is heteregeneous
         from ogb.nodeproppred import PygNodePropPredDataset
 
         # Download and process data at './dataset/.'
 
-        dataset = PygNodePropPredDataset(name=dataset_str,
-                                     transform=torch_geometric.transforms.ToSparseTensor())
+        dataset = PygNodePropPredDataset(
+            name=dataset_str, transform=torch_geometric.transforms.ToSparseTensor()
+        )
 
         split_idx = dataset.get_idx_split()
-        idx_train, idx_val, idx_test = split_idx["train"], split_idx["valid"], split_idx["test"]
+        idx_train, idx_val, idx_test = (
+            split_idx["train"],
+            split_idx["valid"],
+            split_idx["test"],
+        )
         idx_train = torch.LongTensor(idx_train)
         idx_val = torch.LongTensor(idx_val)
         idx_test = torch.LongTensor(idx_test)
         data = dataset[0]
-        
+
         features = data.x
         labels = data.y.reshape(-1)
-        if dataset_str == 'ogbn-arxiv':
+        if dataset_str == "ogbn-arxiv":
             adj = data.adj_t.to_symmetric()
         else:
             adj = data.adj_t
-            
-    elif dataset_str == 'reddit':
+
+    elif dataset_str == "reddit":
         from dgl.data import RedditDataset
+
         data = RedditDataset()
         g = data[0]
-        num_classes = data.num_classes
-        
+
         adj = torch_sparse.tensor.SparseTensor.from_edge_index(g.edges())
 
-        features = g.ndata['feat']
-        train_mask = g.ndata['train_mask']
-        val_mask = g.ndata['val_mask']
-        test_mask = g.ndata['test_mask']
-        
+        features = g.ndata["feat"]
+        train_mask = g.ndata["train_mask"]
+        val_mask = g.ndata["val_mask"]
+        test_mask = g.ndata["test_mask"]
+
         idx_train = (train_mask == True).nonzero().view(-1)
         idx_val = (val_mask == True).nonzero().view(-1)
         idx_test = (test_mask == True).nonzero().view(-1)
-        
-        labels = g.ndata['label']
-
-    return features.float(), adj, labels, idx_train, idx_val, idx_test
-
-
-
-
 
+        labels = g.ndata["label"]
 
+    return features.float(), adj, labels, idx_train, idx_val, idx_test
diff --git a/src/fedgcn_run.py b/src/fedgcn_run.py
index f03bd95..84f3f7b 100644
--- a/src/fedgcn_run.py
+++ b/src/fedgcn_run.py
@@ -1,316 +1,81 @@
-# ---
-# jupyter:
-#   jupytext:
-#     formats: py:light
-#     text_representation:
-#       extension: .py
-#       format_name: light
-#       format_version: '1.5'
-#       jupytext_version: 1.14.4
-#   kernelspec:
-#     display_name: Python 3 (ipykernel)
-#     language: python
-#     name: python3
-# ---
-
-# +
-from time import sleep
-import ray
-import os
 import argparse
+import os
 from pathlib import Path
-import yaml
-
-try: 
-    import ogb
-except:
-    os.system("pip install ogb")
-
-try: 
-    import tensorboard
-except:
-    os.system("pip install tensorboard")
-
+from typing import Any
 
 import numpy as np
+import ray
 import torch
-import torch_geometric
-from torch import Tensor
-from torch_geometric.loader import DataLoader
-
+import yaml
 from torch.utils.tensorboard import SummaryWriter
 
-import os
-
 print(os.getcwd())
 print(os.listdir())
-print(os.listdir('..'))
+print(os.listdir(".."))
 
 import sys
-#adding additional module folders
-sys.path.append(os.path.join(sys.path[0],'src','utility'))
-sys.path.append(os.path.join(sys.path[0],'src','data'))
-
-
-remote = False #false for local simulation
-
-if remote:
-    print(os.listdir('modules'))
-    # GCN model
-    from gnn_models import GCN_Graph_Classification, GCN, GCN_arxiv, SAGE_products, GCN_products
-    from train import test, train, Lhop_Block_matrix_train
-    from utils import label_dirichlet_partition, parition_non_iid, get_in_comm_indexes, get_in_comm_indexes_BDS_GCN, increment_dir, setdiff1d
-    from data_process import generate_data, load_data
-
-else:
-    from gnn_models import GCN_Graph_Classification, GCN, GCN_arxiv, SAGE_products, GCN_products
-    from train import test, train, Lhop_Block_matrix_train
-    from utils import label_dirichlet_partition, parition_non_iid, get_in_comm_indexes, get_in_comm_indexes_BDS_GCN, increment_dir, setdiff1d
-    from data_process import generate_data, load_data
-
-
-# -
-
-
-class Trainer_General:
-    def __init__(self, rank, communicate_index, adj, labels, features, idx_train, idx_test, local_steps, num_layers, args_hidden, class_num, learning_rate, device):
-        # from gnn_models import GCN_Graph_Classification
-        torch.manual_seed(rank)
-
-        # seems that new trainer process will not inherit sys.path from parent, need to reimport!
-        if args.dataset == "ogbn-arxiv":
-            self.model = GCN_arxiv(nfeat=features.shape[1],
-                nhid=args_hidden,
-                nclass=class_num,
-                dropout=0.5,
-                NumLayers=args.num_layers).to(device)
-        elif args.dataset == "ogbn-products":
-            self.model = SAGE_products(nfeat=features.shape[1],
-                nhid=args_hidden,
-                nclass=class_num,
-                dropout=0.5,
-                NumLayers=args.num_layers).to(device)
-        else:
-            self.model = GCN(nfeat=in_feat,
-                            nhid=args_hidden,
-                            nclass=class_num,
-                            dropout=0.5,
-                            NumLayers=args.num_layers).to(device)
-        
-        self.rank = rank #rank = client ID
-        
-        self.device = device
-        
-        self.optimizer = torch.optim.SGD(self.model.parameters(),
-                              lr=learning_rate, weight_decay=5e-4)
-        
-        self.criterion = torch.nn.CrossEntropyLoss()
-        
-        self.train_losses = []
-        self.train_accs = []
-        
-        self.test_losses = []
-        self.test_accs = []
-        
-        self.adj = adj.to(device)
-        self.labels = labels.to(device)
-        self.features = features.to(device)
-        self.idx_train = idx_train.to(device)
-        self.idx_test = idx_test.to(device)
-        
-        self.local_steps = local_steps
-
-  
-    @torch.no_grad()
-    def update_params(self, params, current_global_epoch):
-        #load global parameter from global server
-        self.model.to('cpu')
-        for p, mp, in zip(params, self.model.parameters()):
-            mp.data = p
-        self.model.to(self.device)
-
-    def train(self, current_global_round):
-        #clean cache
-        torch.cuda.empty_cache()
-        for iteration in range(self.local_steps):
-
-            self.model.train()
-            
-            
-            loss_train, acc_train = train(iteration, self.model, self.optimizer, 
-                                          self.features, self.adj, self.labels, self.idx_train)
-            self.train_losses.append(loss_train)
-            self.train_accs.append(acc_train)
-        
-        
-            loss_test, acc_test = local_test_loss, local_test_acc = self.local_test()
-            self.test_losses.append(loss_test)
-            self.test_accs.append(acc_test)
-            
-
-    def local_test(self):
-        local_test_loss, local_test_acc = test(self.model, self.features, self.adj, self.labels, self.idx_test)
-        return [local_test_loss, local_test_acc]
-    
-    def get_params(self):
-        self.optimizer.zero_grad(set_to_none=True)
-        return tuple(self.model.parameters())
-    def get_all_loss_accuray(self):
-        return [np.array(self.train_losses), np.array(self.train_accs), np.array(self.test_losses), np.array(self.test_accs)]
-    def get_rank(self):
-        return self.rank
-
-
-
-class Server:
-    def __init__(self):
-        #server model on cpu
-        if args.dataset == "ogbn-arxiv":
-            self.model = GCN_arxiv(nfeat=features.shape[1],
-                nhid=args_hidden,
-                nclass=class_num,
-                dropout=0.5,
-                NumLayers=args.num_layers)
-        elif args.dataset == "ogbn-products":
-            self.model = SAGE_products(nfeat=features.shape[1],
-                nhid=args_hidden,
-                nclass=class_num,
-                dropout=0.5,
-                NumLayers=args.num_layers)
-        else: #CORA, CITESEER, PUBMED, REDDIT
-            self.model = GCN(nfeat=in_feat,
-                            nhid=args_hidden,
-                            nclass=class_num,
-                            dropout=0.5,
-                            NumLayers=args.num_layers)
-            
-        
-        if device.type == 'cpu':
-            @ray.remote(num_cpus=0.1, scheduling_strategy='SPREAD')
-            class Trainer(Trainer_General):
-                def __init__(self, rank, communicate_index, adj, labels, features, idx_train, idx_test, local_step, num_layers, args_hidden, class_num, learning_rate, device):
-                    super().__init__(rank, communicate_index, adj, labels, features, idx_train, idx_test, local_step, num_layers, args_hidden, class_num, learning_rate, device)
-        
-        elif args.dataset == "ogbn-arxiv":
-            @ray.remote(num_gpus=0.5, num_cpus=5, scheduling_strategy='SPREAD')
-            class Trainer(Trainer_General):
-                def __init__(self, rank, communicate_index, adj, labels, features, idx_train, idx_test, local_step, num_layers, args_hidden, class_num, learning_rate, device):
-                    
-                    super().__init__(rank, communicate_index, adj, labels, features, idx_train, idx_test, local_step, num_layers, args_hidden, class_num, learning_rate, device)
-        else:
-            @ray.remote(num_gpus=1, num_cpus=10, scheduling_strategy='SPREAD')
-            class Trainer(Trainer_General):
-                def __init__(self, rank, communicate_index, adj, labels, features, idx_train, idx_test, local_step, num_layers, args_hidden, class_num, learning_rate, device):
-                    
-                    super().__init__(rank, communicate_index, adj, labels, features, idx_train, idx_test, local_step, num_layers, args_hidden, class_num, learning_rate, device)
-                    
-        if args.fedtype == 'fedsage+':
-            print("running fedsage+")
-            features_in_clients = []
-            #assume the linear generator learnt the optimal (the average of features of neighbor nodes)
-            #gaussian noise
-
-            for i in range(args.n_trainer):
-                #orignial features of outside neighbors of nodes in client i
-                original_feature_i = features[setdiff1d(split_data_indexes[i], communicate_indexes[i])].clone()
-                
-                #add gaussian noise to the communicated feature
-                gaussian_feature_i = original_feature_i + torch.normal(0, 0.1, original_feature_i.shape).cpu()
-
-                copy_feature = features.clone()
-
-                copy_feature[setdiff1d(split_data_indexes[i], communicate_indexes[i])] = gaussian_feature_i
-
-                features_in_clients.append(copy_feature[communicate_indexes[i]])
-
-            self.trainers = [Trainer.remote(i, communicate_indexes[i], edge_indexes_clients[i],
-                                            labels[communicate_indexes[i]], features_in_clients[i],
-                                            in_com_train_data_indexes[i], in_com_test_data_indexes[i], args.local_step, args.num_layers, args_hidden, class_num, args.learning_rate, device) for i in range(args.n_trainer)]   
-        
-        else:
-            self.trainers = [Trainer.remote(i, communicate_indexes[i], edge_indexes_clients[i],
-                                            labels[communicate_indexes[i]], features[communicate_indexes[i]],
-                                            in_com_train_data_indexes[i], in_com_test_data_indexes[i], args.local_step, args.num_layers, args_hidden, class_num, args.learning_rate, device) for i in range(args.n_trainer)]
 
+# adding additional module folders
+sys.path.append(os.path.join(sys.path[0], "src", "utility"))
+sys.path.append(os.path.join(sys.path[0], "src", "data"))
 
-        self.broadcast_params(-1)
-    @torch.no_grad()
-    def zero_params(self):
-        for p in self.model.parameters():
-            p.zero_()
 
-    @torch.no_grad()
-    def train(self, current_global_epoch):
+ray.init()
 
-        for trainer in self.trainers:
-            trainer.train.remote(i)
-        params = [trainer.get_params.remote() for trainer in self.trainers]
-        self.zero_params()
+from data_process import generate_data, load_data
+from gnn_models import GCN, GCN_arxiv, SAGE_products
+from server_class import Server
+from trainer_class import Trainer_General
+from utils import (
+    get_in_comm_indexes,
+    get_in_comm_indexes_BDS_GCN,
+    increment_dir,
+    label_dirichlet_partition,
+    parition_non_iid,
+    setdiff1d,
+)
 
-        while True:
-            ready, left = ray.wait(params, num_returns=1, timeout=None)
-            if ready:
-                for t in ready:
-                    for p, mp in zip(ray.get(t), self.model.parameters()):
-                        mp.data += p.cpu()
-            params = left
-            if not params:
-                break
-
-        for p in self.model.parameters():
-            p /= args.n_trainer
-        self.broadcast_params(current_global_epoch)
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-d", "--dataset", default="cora", type=str)
 
+    parser.add_argument("-f", "--fedtype", default="fedgcn", type=str)
 
-    def broadcast_params(self, current_global_epoch):
-        for trainer in self.trainers:
-            trainer.update_params.remote(tuple(self.model.parameters()), current_global_epoch)  # run in submit order
-# -
+    parser.add_argument("-c", "--global_rounds", default=100, type=int)
+    parser.add_argument("-i", "--local_step", default=3, type=int)
+    parser.add_argument("-lr", "--learning_rate", default=0.5, type=float)
 
+    parser.add_argument("-n", "--n_trainer", default=5, type=int)
+    parser.add_argument("-nl", "--num_layers", default=2, type=int)
+    parser.add_argument("-nhop", "--num_hops", default=2, type=int)
+    parser.add_argument("-g", "--gpu", action="store_true")  # if -g, use gpu
+    parser.add_argument("-iid_b", "--iid_beta", default=10000, type=float)
 
+    parser.add_argument("-l", "--logdir", default="./runs", type=str)
 
-# +
-if __name__=="__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument('-d', '--dataset', default='cora', type=str)
-    
-    parser.add_argument('-f', '--fedtype', default='fedgcn', type=str)
-    
-    parser.add_argument('-c', '--global_rounds',default=100, type=int)
-    parser.add_argument('-i', '--local_step',default=3, type=int)
-    parser.add_argument('-lr', '--learning_rate', default=0.5, type=float)
-    
-    parser.add_argument('-n', '--n_trainer', default=5, type=int)
-    parser.add_argument('-nl', '--num_layers', default=2, type=int)
-    parser.add_argument('-nhop', '--num_hops', default=2, type=int)
-    parser.add_argument('-g', '--gpu', action='store_true') #if -g, use gpu
-    parser.add_argument('-iid_b', '--iid_beta', default=10000, type=float)
-    
-    parser.add_argument('-l', '--logdir', default='./runs', type=str)
-
-    
-    parser.add_argument('-r', '--repeat_time', default=10, type=int)
+    parser.add_argument("-r", "--repeat_time", default=10, type=int)
     args = parser.parse_args()
     print(args)
-    
-    #'cora', 'citeseer', 'pubmed' #simulate #other dataset twitter, 
-    #'ogbn-arxiv', reddit, "ogbn-products"
+
+    # 'cora', 'citeseer', 'pubmed' #simulate #other dataset twitter,
+    # 'ogbn-arxiv', reddit, "ogbn-products"
     np.random.seed(42)
     torch.manual_seed(42)
-    
-    #load data to cpu
-    if args.dataset == 'simulate':
-        number_of_nodes=200
-        class_num=3
-        link_inclass_prob=10/number_of_nodes
-        link_outclass_prob=link_inclass_prob/20
-        features, adj, labels, idx_train, idx_val, idx_test = generate_data(number_of_nodes,  class_num, link_inclass_prob, link_outclass_prob)               
+
+    # load data to cpu
+    if args.dataset == "simulate":
+        number_of_nodes = 200
+        class_num = 3
+        link_inclass_prob = 10 / number_of_nodes
+        link_outclass_prob = link_inclass_prob / 20
+        features, adj, labels, idx_train, idx_val, idx_test = generate_data(
+            number_of_nodes, class_num, link_inclass_prob, link_outclass_prob
+        )
     else:
         features, adj, labels, idx_train, idx_val, idx_test = load_data(args.dataset)
         class_num = labels.max().item() + 1
 
-    in_feat = features.shape[1]
-    if args.dataset in ['simulate', 'cora', 'citeseer', 'pubmed', "reddit"]:
+    if args.dataset in ["simulate", "cora", "citeseer", "pubmed", "reddit"]:
         args_hidden = 16
     else:
         args_hidden = 256
@@ -318,118 +83,273 @@ def broadcast_params(self, current_global_epoch):
     row, col, edge_attr = adj.coo()
     edge_index = torch.stack([row, col], dim=0)
 
-
-    #specifying a target GPU
+    # specifying a target GPU
     if args.gpu:
-        device = torch.device('cuda')
-        #running on a local machine with multiple gpu     
-        if args.dataset == 'ogbn-products':
+        device = torch.device("cuda")
+        # running on a local machine with multiple gpu
+        if args.dataset == "ogbn-products":
             edge_index = edge_index.to("cuda:7")
         else:
             edge_index = edge_index.to("cuda:0")
     else:
-        device = torch.device('cpu')
-        
-    
-    
-    #repeat experiments 
+        device = torch.device("cpu")
+
+    if device.type == "cpu":
+        num_cpus = 0.1
+        num_gpus = 0.0
+    elif args.dataset == "ogbn-arxiv":
+        num_cpus = 5.0
+        num_gpus = 0.5
+    else:
+        num_cpus = 10
+        num_gpus = 1.0
+
+    # repeat experiments
     average_final_test_loss_repeats = []
     average_final_test_accuracy_repeats = []
-    
+
     for repeat in range(args.repeat_time):
+        # load data to cpu
 
-        #load data to cpu
+        # beta = 0.0001 extremely Non-IID, beta = 10000, IID
+        split_data_indexes = label_dirichlet_partition(
+            labels, len(labels), class_num, args.n_trainer, beta=args.iid_beta
+        )
 
-        #beta = 0.0001 extremly Non-IID, beta = 10000, IID
-        split_data_indexes = label_dirichlet_partition(labels, len(labels), class_num, args.n_trainer, beta = args.iid_beta)
-        
         for i in range(args.n_trainer):
             split_data_indexes[i] = np.array(split_data_indexes[i])
             split_data_indexes[i].sort()
             split_data_indexes[i] = torch.tensor(split_data_indexes[i])
-        
-        if args.fedtype == 'bds-gcn':
+
+        if args.fedtype == "bds-gcn":
             print("running bds-gcn")
-            #No args.num_hops
-            communicate_indexes, in_com_train_data_indexes, in_com_test_data_indexes, edge_indexes_clients = get_in_comm_indexes_BDS_GCN(edge_index, split_data_indexes, args.n_trainer, idx_train, idx_test)
+            # No args.num_hops
+            (
+                communicate_indexes,
+                in_com_train_data_indexes,
+                in_com_test_data_indexes,
+                edge_indexes_clients,
+            ) = get_in_comm_indexes_BDS_GCN(
+                edge_index, split_data_indexes, args.n_trainer, idx_train, idx_test
+            )
         else:
-            communicate_indexes, in_com_train_data_indexes, in_com_test_data_indexes, edge_indexes_clients = get_in_comm_indexes(edge_index, split_data_indexes, args.n_trainer, args.num_hops, idx_train, idx_test)
-        
+            (
+                communicate_indexes,
+                in_com_train_data_indexes,
+                in_com_test_data_indexes,
+                edge_indexes_clients,
+            ) = get_in_comm_indexes(
+                edge_index,
+                split_data_indexes,
+                args.n_trainer,
+                args.num_hops,
+                idx_train,
+                idx_test,
+            )
+
+        # determine the resources for each trainer
+        @ray.remote(
+            num_gpus=num_gpus,
+            num_cpus=num_cpus,
+            scheduling_strategy="SPREAD",
+        )
+        class Trainer(Trainer_General):
+            def __init__(self, *args: Any, **kwds: Any):
+                super().__init__(*args, **kwds)
+
+        if args.fedtype == "fedsage+":
+            print("running fedsage+")
+            features_in_clients = []
+            # assume the linear generator learnt the optimal (the average of features of neighbor nodes)
+            # gaussian noise
+
+            for i in range(args.n_trainer):
+                # original features of outside neighbors of nodes in client i
+                original_feature_i = features[
+                    setdiff1d(split_data_indexes[i], communicate_indexes[i])
+                ].clone()
+
+                # add gaussian noise to the communicated feature
+                gaussian_feature_i = (
+                    original_feature_i
+                    + torch.normal(0, 0.1, original_feature_i.shape).cpu()
+                )
+
+                copy_feature = features.clone()
+
+                copy_feature[
+                    setdiff1d(split_data_indexes[i], communicate_indexes[i])
+                ] = gaussian_feature_i
 
-        args.log_dir =  increment_dir(Path(args.logdir) / 'exp')
+                features_in_clients.append(copy_feature[communicate_indexes[i]])
+            trainers = [
+                Trainer.remote(
+                    i,
+                    edge_indexes_clients[i],
+                    labels[communicate_indexes[i]],
+                    features_in_clients[i],
+                    in_com_train_data_indexes[i],
+                    in_com_test_data_indexes[i],
+                    args_hidden,
+                    class_num,
+                    device,
+                    args,
+                )
+                for i in range(args.n_trainer)
+            ]
+        else:
+            trainers = [
+                Trainer.remote(
+                    i,
+                    edge_indexes_clients[i],
+                    labels[communicate_indexes[i]],
+                    features[communicate_indexes[i]],
+                    in_com_train_data_indexes[i],
+                    in_com_test_data_indexes[i],
+                    args_hidden,
+                    class_num,
+                    device,
+                    args,
+                )
+                for i in range(args.n_trainer)
+            ]
+
+        args.log_dir = increment_dir(Path(args.logdir) / "exp")
         os.makedirs(args.log_dir)
         yaml_file = str(Path(args.log_dir) / "args.yaml")
-        with open(yaml_file, 'w') as out:
+        with open(yaml_file, "w") as out:
             yaml.dump(args.__dict__, out, default_flow_style=False)
 
-
         writer = SummaryWriter(args.log_dir)
-        #clear cache
+        # clear cache
         torch.cuda.empty_cache()
-        server = Server()
+        server = Server(
+            features.shape[1], args_hidden, class_num, device, trainers, args
+        )
         print("global_rounds", args.global_rounds)
         for i in range(args.global_rounds):
             server.train(i)
 
-
         results = [trainer.get_all_loss_accuray.remote() for trainer in server.trainers]
         results = np.array([ray.get(result) for result in results])
 
         client_id = 0
         for result in results:
             for iteration in range(len(result[0])):
-                writer.add_scalar('Train Loss/Client_{}'.format(client_id), result[0][iteration], iteration)
+                writer.add_scalar(
+                    "Train Loss/Client_{}".format(client_id),
+                    result[0][iteration],
+                    iteration,
+                )
             for iteration in range(len(result[1])):
-                writer.add_scalar('Train Accuracy/Client_{}'.format(client_id), result[1][iteration], iteration)
+                writer.add_scalar(
+                    "Train Accuracy/Client_{}".format(client_id),
+                    result[1][iteration],
+                    iteration,
+                )
             for iteration in range(len(result[2])):
-                writer.add_scalar('Test Loss/Client_{}.format(client_id)', result[2][iteration], iteration)
+                writer.add_scalar(
+                    "Test Loss/Client_{}.format(client_id)",
+                    result[2][iteration],
+                    iteration,
+                )
             for iteration in range(len(result[3])):
-                writer.add_scalar('Test Accuracy/Client_{}'.format(client_id), result[3][iteration], iteration)
+                writer.add_scalar(
+                    "Test Accuracy/Client_{}".format(client_id),
+                    result[3][iteration],
+                    iteration,
+                )
             client_id += 1
-        #print('finished')
 
         train_data_weights = [len(i) for i in in_com_train_data_indexes]
         test_data_weights = [len(i) for i in in_com_test_data_indexes]
-        average_train_loss = np.average(results[:,0], weights = train_data_weights, axis = 0)
-        average_train_accuracy = np.average(results[:,1], weights = train_data_weights, axis = 0)
-        average_test_loss = np.average(results[:,2], weights = test_data_weights, axis = 0)
-        average_test_accuracy = np.average(results[:,3], weights = test_data_weights, axis = 0)
 
-        for iteration in range(len(results[0][0])):
-            writer.add_scalar('Train Loss/Clients_Overall'.format(), average_train_loss[iteration], iteration)
-            writer.add_scalar('Train Accuracy/Clients_Overall'.format(), average_train_accuracy[iteration], iteration)
-            writer.add_scalar('Test Loss/Clients_Overall'.format(), average_test_loss[iteration], iteration)
-            writer.add_scalar('Train Accuracy/Clients_Overall'.format(), average_test_accuracy[iteration], iteration)
+        average_train_loss = np.average(
+            [row[0] for row in results], weights=train_data_weights, axis=0
+        )
+        average_train_accuracy = np.average(
+            [row[1] for row in results], weights=train_data_weights, axis=0
+        )
+        average_test_loss = np.average(
+            [row[2] for row in results], weights=test_data_weights, axis=0
+        )
+        average_test_accuracy = np.average(
+            [row[3] for row in results], weights=test_data_weights, axis=0
+        )
 
-        
+        for iteration in range(len(results[0][0])):
+            writer.add_scalar(
+                "Train Loss/Clients_Overall".format(),
+                average_train_loss[iteration],
+                iteration,
+            )
+            writer.add_scalar(
+                "Train Accuracy/Clients_Overall".format(),
+                average_train_accuracy[iteration],
+                iteration,
+            )
+            writer.add_scalar(
+                "Test Loss/Clients_Overall".format(),
+                average_test_loss[iteration],
+                iteration,
+            )
+            writer.add_scalar(
+                "Train Accuracy/Clients_Overall".format(),
+                average_test_accuracy[iteration],
+                iteration,
+            )
 
-        
         results = [trainer.local_test.remote() for trainer in server.trainers]
         results = np.array([ray.get(result) for result in results])
-        
-        average_final_test_loss = np.average(results[:,0], weights = test_data_weights, axis = 0)
-        average_final_test_accuracy = np.average(results[:,1], weights = test_data_weights, axis = 0)
-        
-        print(average_final_test_loss, average_final_test_accuracy)
-        
-        #sleep(5)  # wait for print message from remote workers
-        filename = args.dataset + "_" + args.fedtype + "_" + str(args.num_layers) + "_layer_" + str(args.num_hops) + "_hop_iid_beta_" + str(args.iid_beta) + "_n_trainer_" + str(args.n_trainer) + "_local_step_" + str(args.local_step) + ".txt"
-        with open(filename, 'a+') as a:
-            a.write(f'{average_final_test_loss} {average_final_test_accuracy}\n')
-            average_final_test_loss_repeats.append(average_final_test_loss)
-            average_final_test_accuracy_repeats.append(average_final_test_accuracy)
-            
-    #finish experiments
-    with open(f'{args.dataset}_{args.fedtype}_{args.num_layers}_layer_{args.num_hops}_hop_iid_beta_{args.iid_beta}_n_trainer_{args.n_trainer}_local_step_{args.local_step}.txt', 'a+') as a:
-        a.write(f'average_testing_loss {np.average(average_final_test_loss_repeats)} std {np.std(average_final_test_loss_repeats)}\n')
-        a.write(f'average_testing_accuracy {np.average(average_final_test_accuracy_repeats)} std {np.std(average_final_test_accuracy_repeats)}\n')
-        
-    print(f'average_testing_loss {np.average(average_final_test_loss_repeats)} std {np.std(average_final_test_loss_repeats)}')
-    print(f'average_testing_accuracy {np.average(average_final_test_accuracy_repeats)} std {np.std(average_final_test_accuracy_repeats)}')
 
-ray.shutdown()
+        average_final_test_loss = np.average(
+            [row[0] for row in results], weights=test_data_weights, axis=0
+        )
+        average_final_test_accuracy = np.average(
+            [row[1] for row in results], weights=test_data_weights, axis=0
+        )
 
+        print(average_final_test_loss, average_final_test_accuracy)
 
+        # sleep(5)  # wait for print message from remote workers
+        filename = (
+            args.dataset
+            + "_"
+            + args.fedtype
+            + "_"
+            + str(args.num_layers)
+            + "_layer_"
+            + str(args.num_hops)
+            + "_hop_iid_beta_"
+            + str(args.iid_beta)
+            + "_n_trainer_"
+            + str(args.n_trainer)
+            + "_local_step_"
+            + str(args.local_step)
+            + ".txt"
+        )
+        with open(filename, "a+") as a:
+            a.write(f"{average_final_test_loss} {average_final_test_accuracy}\n")
+            average_final_test_loss_repeats.append(average_final_test_loss)
+            average_final_test_accuracy_repeats.append(average_final_test_accuracy)
 
-# -
+    # finish experiments
+    with open(
+        f"{args.dataset}_{args.fedtype}_{args.num_layers}_layer_{args.num_hops}_hop_iid_beta_{args.iid_beta}_n_trainer_{args.n_trainer}_local_step_{args.local_step}.txt",
+        "a+",
+    ) as a:
+        a.write(
+            f"average_testing_loss {np.average(average_final_test_loss_repeats)} std {np.std(average_final_test_loss_repeats)}\n"
+        )
+        a.write(
+            f"average_testing_accuracy {np.average(average_final_test_accuracy_repeats)} std {np.std(average_final_test_accuracy_repeats)}\n"
+        )
+
+    print(
+        f"average_testing_loss {np.average(average_final_test_loss_repeats)} std {np.std(average_final_test_loss_repeats)}"
+    )
+    print(
+        f"average_testing_accuracy {np.average(average_final_test_accuracy_repeats)} std {np.std(average_final_test_accuracy_repeats)}"
+    )
 
+ray.shutdown()
diff --git a/src/gnn_models.py b/src/gnn_models.py
index 7ae3193..0b70dda 100644
--- a/src/gnn_models.py
+++ b/src/gnn_models.py
@@ -1,12 +1,14 @@
 import torch
-from torch.nn import Linear
 import torch.nn.functional as F
-from torch_geometric.nn import GCNConv, SAGEConv
-from torch_geometric.nn import global_mean_pool
+from torch.nn import Linear
+from torch_geometric.nn import GCNConv, SAGEConv, global_mean_pool
+
 
 class GCN(torch.nn.Module):
-    def __init__(self, nfeat: int, nhid: int, nclass: int, dropout: float, NumLayers: int):
-        '''
+    def __init__(
+        self, nfeat: int, nhid: int, nclass: int, dropout: float, NumLayers: int
+    ):
+        """
         This constructor method initializes the GCN model
 
         Arguments:
@@ -14,30 +16,28 @@ def __init__(self, nfeat: int, nhid: int, nclass: int, dropout: float, NumLayers
         nhid: (int) - Number of hidden features in the hidden layers of the network
         nclass: (int) - Number of output classes
         dropout: (float) - Dropout probability
-        NumLayers: (int) - Number of GCN layers in the network.        
-        '''
+        NumLayers: (int) - Number of GCN layers in the network.
+        """
         super(GCN, self).__init__()
 
         self.convs = torch.nn.ModuleList()
-        self.convs.append(
-            GCNConv(nfeat, nhid, normalize=True, cached=True))
+        self.convs.append(GCNConv(nfeat, nhid, normalize=True, cached=True))
         for _ in range(NumLayers - 2):
-            self.convs.append(
-                GCNConv(nhid, nhid, normalize=True, cached=True))
-        self.convs.append(
-            GCNConv(nhid, nclass, normalize=True, cached=True))
+            self.convs.append(GCNConv(nhid, nhid, normalize=True, cached=True))
+        self.convs.append(GCNConv(nhid, nclass, normalize=True, cached=True))
 
         self.dropout = dropout
 
-    def reset_parameters(self):
-        '''
-        This function is available to cater to weight initialization requirements as necessary.      
-        '''
+    def reset_parameters(self) -> None:
+        """
+        This function is available to cater to weight initialization requirements as necessary.
+        """
         for conv in self.convs:
             conv.reset_parameters()
+        return None
 
     def forward(self, x: torch.Tensor, adj_t: torch.Tensor) -> torch.Tensor:
-        '''
+        """
         This function represents the forward pass computation of a GCN
 
         Arguments:
@@ -47,7 +47,7 @@ def forward(self, x: torch.Tensor, adj_t: torch.Tensor) -> torch.Tensor:
         Returns:
         The output of the forward pass, a PyTorch tensor
 
-        '''
+        """
         for conv in self.convs[:-1]:
             x = conv(x, adj_t)
             x = F.relu(x)
@@ -55,9 +55,12 @@ def forward(self, x: torch.Tensor, adj_t: torch.Tensor) -> torch.Tensor:
         x = self.convs[-1](x, adj_t)
         return torch.log_softmax(x, dim=-1)
 
+
 class GCN_products(torch.nn.Module):
-    def __init__(self, nfeat: int, nhid: int, nclass: int, dropout: float, NumLayers: int):
-        '''
+    def __init__(
+        self, nfeat: int, nhid: int, nclass: int, dropout: float, NumLayers: int
+    ):
+        """
         This constructor method initializes the GCN_products model
 
         Arguments:
@@ -65,31 +68,29 @@ def __init__(self, nfeat: int, nhid: int, nclass: int, dropout: float, NumLayers
         nhid: (int) - Number of hidden features in the hidden layers of the network
         nclass: (int) - Number of output classes
         dropout: (float) - Dropout probability
-        NumLayers: (int) - Number of GCN layers in the network.        
-        '''
+        NumLayers: (int) - Number of GCN layers in the network.
+        """
 
         super(GCN_products, self).__init__()
-    
+
         self.convs = torch.nn.ModuleList()
-        self.convs.append(
-            GCNConv(nfeat, nhid, normalize=False))
+        self.convs.append(GCNConv(nfeat, nhid, normalize=False))
         for _ in range(NumLayers - 2):
-            self.convs.append(
-                GCNConv(nhid, nhid, normalize=False))
-        self.convs.append(
-            GCNConv(nhid, nclass, normalize=False))
+            self.convs.append(GCNConv(nhid, nhid, normalize=False))
+        self.convs.append(GCNConv(nhid, nclass, normalize=False))
 
         self.dropout = dropout
 
-    def reset_parameters(self):
-        '''
-        This function is available to cater to weight initialization requirements as necessary.      
-        '''
+    def reset_parameters(self) -> None:
+        """
+        This function is available to cater to weight initialization requirements as necessary.
+        """
         for conv in self.convs:
             conv.reset_parameters()
+        return None
 
     def forward(self, x: torch.Tensor, adj_t: torch.Tensor) -> torch.Tensor:
-        '''
+        """
         This function represents the forward pass computation of a GCN
 
         Arguments:
@@ -98,18 +99,21 @@ def forward(self, x: torch.Tensor, adj_t: torch.Tensor) -> torch.Tensor:
 
         Returns:
         The output of the forward pass, a PyTorch tensor
-        
-        '''
+
+        """
         for conv in self.convs[:-1]:
             x = conv(x, adj_t)
             x = F.relu(x)
             x = F.dropout(x, p=self.dropout, training=self.training)
         x = self.convs[-1](x, adj_t)
         return torch.log_softmax(x, dim=-1)
-    
+
+
 class SAGE_products(torch.nn.Module):
-    def __init__(self, nfeat: int, nhid: int, nclass: int, dropout: float, NumLayers: int):
-        '''
+    def __init__(
+        self, nfeat: int, nhid: int, nclass: int, dropout: float, NumLayers: int
+    ):
+        """
         This constructor method initializes the Graph Sage model
 
         Arguments:
@@ -117,8 +121,8 @@ def __init__(self, nfeat: int, nhid: int, nclass: int, dropout: float, NumLayers
         nhid: (int) - Number of hidden features in the hidden layers of the network
         nclass: (int) - Number of output classes
         dropout: (float) - Dropout probability
-        NumLayers: (int) - Number of Graph Sage layers in the network     
-        '''
+        NumLayers: (int) - Number of Graph Sage layers in the network
+        """
         super(SAGE_products, self).__init__()
 
         self.convs = torch.nn.ModuleList()
@@ -129,15 +133,16 @@ def __init__(self, nfeat: int, nhid: int, nclass: int, dropout: float, NumLayers
 
         self.dropout = dropout
 
-    def reset_parameters(self):
-        '''
-        This function is available to cater to weight initialization requirements as necessary.      
-        '''
+    def reset_parameters(self) -> None:
+        """
+        This function is available to cater to weight initialization requirements as necessary.
+        """
         for conv in self.convs:
             conv.reset_parameters()
+        return None
 
     def forward(self, x: torch.Tensor, adj_t: torch.Tensor) -> torch.Tensor:
-        '''
+        """
         This function represents the forward pass computation of a GCN
 
         Arguments:
@@ -146,19 +151,22 @@ def forward(self, x: torch.Tensor, adj_t: torch.Tensor) -> torch.Tensor:
 
         Returns:
         The output of the forward pass, a PyTorch tensor
-        
-        '''
+
+        """
         for conv in self.convs[:-1]:
             x = conv(x, adj_t)
             x = F.relu(x)
             x = F.dropout(x, p=self.dropout, training=self.training)
         x = self.convs[-1](x, adj_t)
         return torch.log_softmax(x, dim=-1)
-    
+
+
 # +
 class GCN_arxiv(torch.nn.Module):
-    def __init__(self, nfeat: int, nhid: int, nclass: int, dropout: float, NumLayers: int):
-        '''
+    def __init__(
+        self, nfeat: int, nhid: int, nclass: int, dropout: float, NumLayers: int
+    ):
+        """
         This constructor method initializes the Graph Sage model
 
         Arguments:
@@ -166,8 +174,8 @@ def __init__(self, nfeat: int, nhid: int, nclass: int, dropout: float, NumLayers
         nhid: (int) - Number of hidden features in the hidden layers of the network
         nclass: (int) - Number of output classes
         dropout: (float) - Dropout probability
-        NumLayers: (int) - Number of Graph Sage layers in the network     
-        '''
+        NumLayers: (int) - Number of Graph Sage layers in the network
+        """
         super(GCN_arxiv, self).__init__()
 
         self.convs = torch.nn.ModuleList()
@@ -175,24 +183,24 @@ def __init__(self, nfeat: int, nhid: int, nclass: int, dropout: float, NumLayers
         self.bns = torch.nn.ModuleList()
         self.bns.append(torch.nn.BatchNorm1d(nhid))
         for _ in range(NumLayers - 2):
-            self.convs.append(
-                GCNConv(nhid, nhid, cached=True))
+            self.convs.append(GCNConv(nhid, nhid, cached=True))
             self.bns.append(torch.nn.BatchNorm1d(nhid))
         self.convs.append(GCNConv(nhid, nclass, cached=True))
 
         self.dropout = dropout
 
-    def reset_parameters(self):
-        '''
-        This function is available to cater to weight initialization requirements as necessary.      
-        '''
+    def reset_parameters(self) -> None:
+        """
+        This function is available to cater to weight initialization requirements as necessary.
+        """
         for conv in self.convs:
             conv.reset_parameters()
         for bn in self.bns:
             bn.reset_parameters()
+        return None
 
     def forward(self, x: torch.Tensor, adj_t: torch.Tensor) -> torch.Tensor:
-        '''
+        """
         This function represents the forward pass computation of a GCN
 
         Arguments:
@@ -201,8 +209,8 @@ def forward(self, x: torch.Tensor, adj_t: torch.Tensor) -> torch.Tensor:
 
         Returns:
         The output of the forward pass, a PyTorch tensor
-        
-        '''
+
+        """
         for i, conv in enumerate(self.convs[:-1]):
             x = conv(x, adj_t)
             x = self.bns[i](x)
@@ -210,34 +218,3 @@ def forward(self, x: torch.Tensor, adj_t: torch.Tensor) -> torch.Tensor:
             x = F.dropout(x, p=self.dropout, training=self.training)
         x = self.convs[-1](x, adj_t)
         return x.log_softmax(dim=-1)
-    
-
-
-# -
-#REDUNDANT
-
-class GCN_Graph_Classification(torch.nn.Module):
-    def __init__(self, num_node_features, nhid, num_classes):
-        super(GCN_Graph_Classification, self).__init__()
-        torch.manual_seed(12345)
-        self.conv1 = GCNConv(num_node_features, nhid)
-        self.conv2 = GCNConv(nhid, nhid)
-        self.conv3 = GCNConv(nhid, nhid)
-        self.lin = Linear(nhid, num_classes)
-
-    def forward(self, x, edge_index, batch):
-        # 1. Obtain node embeddings 
-        x = self.conv1(x, edge_index)
-        x = x.relu()
-        x = self.conv2(x, edge_index)
-        x = x.relu()
-        x = self.conv3(x, edge_index)
-
-        # 2. Readout layer
-        x = global_mean_pool(x, batch)  # [batch_size, nhid]
-
-        # 3. Apply a final classifier
-        x = F.dropout(x, p=0.5, training=self.training)
-        x = self.lin(x)
-        
-        return x
diff --git a/src/server_class.py b/src/server_class.py
new file mode 100644
index 0000000..1a2be2c
--- /dev/null
+++ b/src/server_class.py
@@ -0,0 +1,79 @@
+from typing import Any
+
+import ray
+import torch
+from gnn_models import GCN, GCN_arxiv, SAGE_products
+from trainer_class import Trainer_General
+
+
+class Server:
+    def __init__(
+        self,
+        feature_dim: int,
+        args_hidden: int,
+        class_num: int,
+        device: torch.device,
+        trainers: list[Trainer_General],
+        args: Any,
+    ) -> None:
+        # server model on cpu
+        if args.dataset == "ogbn-arxiv":
+            self.model = GCN_arxiv(
+                nfeat=feature_dim,
+                nhid=args_hidden,
+                nclass=class_num,
+                dropout=0.5,
+                NumLayers=args.num_layers,
+            )
+        elif args.dataset == "ogbn-products":
+            self.model = SAGE_products(
+                nfeat=feature_dim,
+                nhid=args_hidden,
+                nclass=class_num,
+                dropout=0.5,
+                NumLayers=args.num_layers,
+            )
+        else:  # CORA, CITESEER, PUBMED, REDDIT
+            self.model = GCN(
+                nfeat=feature_dim,
+                nhid=args_hidden,
+                nclass=class_num,
+                dropout=0.5,
+                NumLayers=args.num_layers,
+            )
+
+        self.trainers = trainers
+        self.num_of_trainers = len(trainers)
+        self.broadcast_params(-1)
+
+    @torch.no_grad()
+    def zero_params(self) -> None:
+        for p in self.model.parameters():
+            p.zero_()
+
+    @torch.no_grad()
+    def train(self, current_global_epoch: int) -> None:
+        for trainer in self.trainers:
+            trainer.train.remote(current_global_epoch)
+        params = [trainer.get_params.remote() for trainer in self.trainers]
+        self.zero_params()
+
+        while True:
+            ready, left = ray.wait(params, num_returns=1, timeout=None)
+            if ready:
+                for t in ready:
+                    for p, mp in zip(ray.get(t), self.model.parameters()):
+                        mp.data += p.cpu()
+            params = left
+            if not params:
+                break
+
+        for p in self.model.parameters():
+            p /= self.num_of_trainers
+        self.broadcast_params(current_global_epoch)
+
+    def broadcast_params(self, current_global_epoch: int) -> None:
+        for trainer in self.trainers:
+            trainer.update_params.remote(
+                tuple(self.model.parameters()), current_global_epoch
+            )  # run in submit order
diff --git a/src/train.py b/src/train_func.py
similarity index 64%
rename from src/train.py
rename to src/train_func.py
index acb440d..90482f1 100644
--- a/src/train.py
+++ b/src/train_func.py
@@ -1,9 +1,9 @@
 import torch
 import torch.nn.functional as F
-from sklearn import metrics
 
-def accuracy(output: torch.Tensor, labels: torch.Tensor) -> float:
-    '''
+
+def accuracy(output: torch.Tensor, labels: torch.Tensor) -> torch.Tensor:
+    """
     This function returns the accuracy of the output with respect to the ground truth given
 
     Arguments:
@@ -13,17 +13,24 @@ def accuracy(output: torch.Tensor, labels: torch.Tensor) -> float:
 
     Returns:
     The accuracy of the model (float)
-    '''
+    """
 
     preds = output.max(1)[1].type_as(labels)
     correct = preds.eq(labels).double()
     correct = correct.sum()
     return correct / len(labels)
 
-def test(model: torch.nn.Module, features: torch.Tensor, adj: torch.Tensor, labels: torch.Tensor, idx_test: torch.Tensor) -> tuple:
-    '''
-    This function tests the model and calculates the loss and accuracy 
-    
+
+def test(
+    model: torch.nn.Module,
+    features: torch.Tensor,
+    adj: torch.Tensor,
+    labels: torch.Tensor,
+    idx_test: torch.Tensor,
+) -> tuple:
+    """
+    This function tests the model and calculates the loss and accuracy
+
     Arguments:
     model: (torch.nn.Module) - Specific model passed
     features: (torch.Tensor) - Tensor representing the input features
@@ -33,22 +40,29 @@ def test(model: torch.nn.Module, features: torch.Tensor, adj: torch.Tensor, labe
 
     Returns:
     The loss and accuracy of the model
-    
-    '''
+
+    """
     model.eval()
     output = model(features, adj)
-    pred_labels=torch.argmax(output,axis=1)
+    pred_labels = torch.argmax(output, axis=1)
     loss_test = F.nll_loss(output[idx_test], labels[idx_test])
     acc_test = accuracy(output[idx_test], labels[idx_test])
-        
-    return loss_test.item(), acc_test.item()#, f1_test, auc_test
+
+    return loss_test.item(), acc_test.item()  # , f1_test, auc_test
 
 
+def train(
+    epoch: int,
+    model: torch.nn.Module,
+    optimizer: torch.optim.Optimizer,
+    features: torch.Tensor,
+    adj: torch.Tensor,
+    labels: torch.Tensor,
+    idx_train: torch.Tensor,
+) -> tuple:  # Centralized or new FL
+    """
+    This function trains the model and returns the loss and accuracy
 
-def train(epoch: int, model: torch.nn.Module, optimizer: torch.optim.Optimizer, features: torch.Tensor, adj: torch.Tensor, labels: torch.Tensor, idx_train: torch.Tensor) -> tuple:  #Centralized or new FL
-    '''
-    This function trains the model and returns the loss and accuracy 
-    
     Arguments:
     model: (torch.nn.Module) - Specific model passed
     features: (torch.FloatTensor) - Tensor representing the input features
@@ -60,24 +74,33 @@ def train(epoch: int, model: torch.nn.Module, optimizer: torch.optim.Optimizer,
 
     Returns:
     The loss and accuracy of the model
-    
-    '''
-    
+
+    """
+
     model.train()
     optimizer.zero_grad()
-    
+
     output = model(features, adj)
     loss_train = F.nll_loss(output[idx_train], labels[idx_train])
     acc_train = accuracy(output[idx_train], labels[idx_train])
     loss_train.backward()
     optimizer.step()
     optimizer.zero_grad()
-    
+
     return loss_train.item(), acc_train.item()
 
 
-def Lhop_Block_matrix_train(epoch: int, model: torch.nn.Module, optimizer: torch.optim.Optimizer, features: torch.Tensor, adj: torch.Tensor, labels: torch.Tensor, communicate_index: torch.Tensor, in_com_train_data_index: torch.Tensor) -> tuple:
-    '''    
+def Lhop_Block_matrix_train(
+    epoch: int,
+    model: torch.nn.Module,
+    optimizer: torch.optim.Optimizer,
+    features: torch.Tensor,
+    adj: torch.Tensor,
+    labels: torch.Tensor,
+    communicate_index: torch.Tensor,
+    in_com_train_data_index: torch.Tensor,
+) -> tuple:
+    """
     Arguments:
     model: (model type) - Specific model passed
     features: (torch.FloatTensor) - Tensor representing the input features
@@ -90,30 +113,45 @@ def Lhop_Block_matrix_train(epoch: int, model: torch.nn.Module, optimizer: torch
 
     Returns:
     The loss and accuracy of the model
-    
-    '''
+
+    """
 
     model.train()
     optimizer.zero_grad()
 
-    output = model(features[communicate_index], adj[communicate_index][:,communicate_index])
-   
-    
-    loss_train = F.nll_loss(output[in_com_train_data_index], labels[communicate_index][in_com_train_data_index])
-    
-    
-    acc_train = accuracy(output[in_com_train_data_index], labels[communicate_index][in_com_train_data_index])
-    
+    output = model(
+        features[communicate_index], adj[communicate_index][:, communicate_index]
+    )
+
+    loss_train = F.nll_loss(
+        output[in_com_train_data_index],
+        labels[communicate_index][in_com_train_data_index],
+    )
+
+    acc_train = accuracy(
+        output[in_com_train_data_index],
+        labels[communicate_index][in_com_train_data_index],
+    )
 
     loss_train.backward()
     optimizer.step()
     optimizer.zero_grad()
     return loss_train.item(), acc_train.item()
 
-def FedSage_train(epoch: int, model: torch.nn.Module, optimizer: torch.optim.Optimizer, features: torch.Tensor, adj: torch.Tensor, labels: torch.Tensor, communicate_index: torch.Tensor, in_com_train_data_index: torch.Tensor) -> tuple:
-    '''
+
+def FedSage_train(
+    epoch: int,
+    model: torch.nn.Module,
+    optimizer: torch.optim.Optimizer,
+    features: torch.Tensor,
+    adj: torch.Tensor,
+    labels: torch.Tensor,
+    communicate_index: torch.Tensor,
+    in_com_train_data_index: torch.Tensor,
+) -> tuple:
+    """
     This function is to train the FedSage model
-    
+
     Arguments:
     model: (model type) - Specific model passed
     features: (torch.FloatTensor) - Tensor representing the input features
@@ -126,20 +164,24 @@ def FedSage_train(epoch: int, model: torch.nn.Module, optimizer: torch.optim.Opt
 
     Returns:
     The loss and accuracy of the model
-    
-    '''
-    
+
+    """
+
     model.train()
     optimizer.zero_grad()
-    #print(features.shape)   
-    
-    output = model(features, adj[communicate_index][:,communicate_index])
-   
-    loss_train = F.nll_loss(output[in_com_train_data_index], labels[communicate_index][in_com_train_data_index])
-    
-    
-    acc_train = accuracy(output[in_com_train_data_index], labels[communicate_index][in_com_train_data_index])
-    
+    # print(features.shape)
+
+    output = model(features, adj[communicate_index][:, communicate_index])
+
+    loss_train = F.nll_loss(
+        output[in_com_train_data_index],
+        labels[communicate_index][in_com_train_data_index],
+    )
+
+    acc_train = accuracy(
+        output[in_com_train_data_index],
+        labels[communicate_index][in_com_train_data_index],
+    )
 
     loss_train.backward()
     optimizer.step()
diff --git a/src/trainer_class.py b/src/trainer_class.py
new file mode 100644
index 0000000..5f4deeb
--- /dev/null
+++ b/src/trainer_class.py
@@ -0,0 +1,128 @@
+from typing import Any
+
+import numpy as np
+import torch
+from gnn_models import GCN, GCN_arxiv, SAGE_products
+from train_func import test, train
+
+
+class Trainer_General:
+    def __init__(
+        self,
+        rank: int,
+        adj: torch.Tensor,
+        labels: torch.Tensor,
+        features: torch.Tensor,
+        idx_train: torch.Tensor,
+        idx_test: torch.Tensor,
+        args_hidden: int,
+        class_num: int,
+        device: torch.device,
+        args: Any,
+    ):
+        # from gnn_models import GCN_Graph_Classification
+        torch.manual_seed(rank)
+
+        # seems that new trainer process will not inherit sys.path from parent, need to reimport!
+        if args.dataset == "ogbn-arxiv":
+            self.model = GCN_arxiv(
+                nfeat=features.shape[1],
+                nhid=args_hidden,
+                nclass=class_num,
+                dropout=0.5,
+                NumLayers=args.num_layers,
+            ).to(device)
+        elif args.dataset == "ogbn-products":
+            self.model = SAGE_products(
+                nfeat=features.shape[1],
+                nhid=args_hidden,
+                nclass=class_num,
+                dropout=0.5,
+                NumLayers=args.num_layers,
+            ).to(device)
+        else:
+            self.model = GCN(
+                nfeat=features.shape[1],
+                nhid=args_hidden,
+                nclass=class_num,
+                dropout=0.5,
+                NumLayers=args.num_layers,
+            ).to(device)
+
+        self.rank = rank  # rank = client ID
+
+        self.device = device
+
+        self.optimizer = torch.optim.SGD(
+            self.model.parameters(), lr=args.learning_rate, weight_decay=5e-4
+        )
+
+        self.criterion = torch.nn.CrossEntropyLoss()
+
+        self.train_losses: list = []
+        self.train_accs: list = []
+
+        self.test_losses: list = []
+        self.test_accs: list = []
+
+        self.adj = adj.to(device)
+        self.labels = labels.to(device)
+        self.features = features.to(device)
+        self.idx_train = idx_train.to(device)
+        self.idx_test = idx_test.to(device)
+
+        self.local_step = args.local_step
+
+    @torch.no_grad()
+    def update_params(self, params: tuple, current_global_epoch: int) -> None:
+        # load global parameter from global server
+        self.model.to("cpu")
+        for (
+            p,
+            mp,
+        ) in zip(params, self.model.parameters()):
+            mp.data = p
+        self.model.to(self.device)
+
+    def train(self, current_global_round: int) -> None:
+        # clean cache
+        torch.cuda.empty_cache()
+        for iteration in range(self.local_step):
+            self.model.train()
+
+            loss_train, acc_train = train(
+                iteration,
+                self.model,
+                self.optimizer,
+                self.features,
+                self.adj,
+                self.labels,
+                self.idx_train,
+            )
+            self.train_losses.append(loss_train)
+            self.train_accs.append(acc_train)
+
+            loss_test, acc_test = self.local_test()
+            self.test_losses.append(loss_test)
+            self.test_accs.append(acc_test)
+
+    def local_test(self) -> list:
+        local_test_loss, local_test_acc = test(
+            self.model, self.features, self.adj, self.labels, self.idx_test
+        )
+        return [local_test_loss, local_test_acc]
+
+    def get_params(self) -> tuple:
+        self.optimizer.zero_grad(set_to_none=True)
+        return tuple(self.model.parameters())
+
+    def get_all_loss_accuray(self) -> list:
+        return [
+            np.array(self.train_losses),
+            np.array(self.train_accs),
+            np.array(self.test_losses),
+            np.array(self.test_accs),
+        ]
+
+    def get_rank(self) -> int:
+        return self.rank
diff --git a/src/utils.py b/src/utils.py
index bf7e3bc..4130e7d 100644
--- a/src/utils.py
+++ b/src/utils.py
@@ -1,13 +1,15 @@
-import torch 
-import numpy as np
-import torch_geometric
-from pathlib import Path
 import glob
 import re
+from pathlib import Path
+
+import numpy as np
+import torch
+import torch_geometric
 import torch_sparse
 
+
 def intersect1d(t1: torch.Tensor, t2: torch.Tensor) -> torch.Tensor:
-    '''
+    """
     This function concatenates the two input tensors, finding common elements between these two
 
     Argument:
@@ -16,7 +18,7 @@ def intersect1d(t1: torch.Tensor, t2: torch.Tensor) -> torch.Tensor:
 
     Return:
     intersection: (PyTorch tensor) - Intersection of the two input tensors
-    '''
+    """
     combined = torch.cat((t1, t2))
     uniques, counts = combined.unique(return_counts=True)
     intersection = uniques[counts > 1]
@@ -24,26 +26,28 @@ def intersect1d(t1: torch.Tensor, t2: torch.Tensor) -> torch.Tensor:
 
 
 def setdiff1d(t1: torch.Tensor, t2: torch.Tensor) -> torch.Tensor:
-    '''
-    This function computes the set difference between the two input tensors 
+    """
+    This function computes the set difference between the two input tensors
 
     Arguments:
     t1: (PyTorch tensor) - The first input tensor for the operation
     t2: (PyTorch tensor) - The second input tensor for the operation
 
     Return:
-    difference: (PyTorch tensor) - Difference in elements of the two input tensors 
+    difference: (PyTorch tensor) - Difference in elements of the two input tensors
+
+    """
 
-    '''
-    
     combined = torch.cat((t1, t2))
     uniques, counts = combined.unique(return_counts=True)
     difference = uniques[counts == 1]
     return difference
 
 
-def label_dirichlet_partition(labels: np.array, N: int, K: int, n_parties: int, beta: float) -> list:
-    '''
+def label_dirichlet_partition(
+    labels: np.array, N: int, K: int, n_parties: int, beta: float
+) -> list:
+    """
     This function partitions data based on labels by using the Dirichlet distribution, to ensure even distribution of samples
 
     Arguments:
@@ -56,26 +60,34 @@ def label_dirichlet_partition(labels: np.array, N: int, K: int, n_parties: int,
     Return:
     split_data_indexes (list) - list indices of data points assigned into groups
 
-    '''
+    """
     min_size = 0
     min_require_size = 10
 
     split_data_indexes = []
 
     while min_size < min_require_size:
-        idx_batch = [[] for _ in range(n_parties)]
+        idx_batch: list[list[int]] = [[] for _ in range(n_parties)]
         for k in range(K):
             idx_k = np.where(labels == k)[0]
             np.random.shuffle(idx_k)
             proportions = np.random.dirichlet(np.repeat(beta, n_parties))
-            
-            proportions = np.array([p * (len(idx_j) < N / n_parties) for p, idx_j in zip(proportions, idx_batch)])
-            
+
+            proportions = np.array(
+                [
+                    p * (len(idx_j) < N / n_parties)
+                    for p, idx_j in zip(proportions, idx_batch)
+                ]
+            )
+
             proportions = proportions / proportions.sum()
-            
+
             proportions = (np.cumsum(proportions) * len(idx_k)).astype(int)[:-1]
-            
-            idx_batch = [idx_j + idx.tolist() for idx_j, idx in zip(idx_batch, np.split(idx_k, proportions))]
+
+            idx_batch = [
+                idx_j + idx.tolist()
+                for idx_j, idx in zip(idx_batch, np.split(idx_k, proportions))
+            ]
             min_size = min([len(idx_j) for idx_j in idx_batch])
 
     for j in range(n_parties):
@@ -84,7 +96,13 @@ def label_dirichlet_partition(labels: np.array, N: int, K: int, n_parties: int,
     return split_data_indexes
 
 
-def parition_non_iid(non_iid_percent: float, labels: torch.Tensor, num_clients: int, nclass: int, args_cuda: bool) -> list:
+def parition_non_iid(
+    non_iid_percent: float,
+    labels: torch.Tensor,
+    num_clients: int,
+    nclass: int,
+    args_cuda: bool,
+) -> list:
     """
     This function partitions data into non-IID subsets.
 
@@ -100,41 +118,67 @@ def parition_non_iid(non_iid_percent: float, labels: torch.Tensor, num_clients:
     """
 
     split_data_indexes = []
-    iid_indexes = [] #random assign
-    shuffle_labels = [] #make train data points split into different devices
+    iid_indexes = []  # random assign
+    shuffle_labels = []  # make train data points split into different devices
     for i in range(num_clients):
         current = torch.nonzero(labels == i).reshape(-1)
-        current = current[np.random.permutation(len(current))] #shuffle
+        current = current[np.random.permutation(len(current))]  # shuffle
         shuffle_labels.append(current)
 
     average_device_of_class = num_clients // nclass
-    if num_clients % nclass != 0: #for non-iid
+    if num_clients % nclass != 0:  # for non-iid
         average_device_of_class += 1
     for i in range(num_clients):
-        label_i= i // average_device_of_class    
+        label_i = i // average_device_of_class
         labels_class = shuffle_labels[label_i]
 
-        average_num= int(len(labels_class)//average_device_of_class * non_iid_percent)
-        split_data_indexes.append((labels_class[average_num * (i % average_device_of_class):average_num * (i % average_device_of_class + 1)]))
+        average_num = int(
+            len(labels_class) // average_device_of_class * non_iid_percent
+        )
+        split_data_indexes.append(
+            (
+                labels_class[
+                    average_num
+                    * (i % average_device_of_class) : average_num
+                    * (i % average_device_of_class + 1)
+                ]
+            )
+        )
 
     if args_cuda:
-        iid_indexes = setdiff1d(torch.tensor(range(len(labels))).cuda(), torch.cat(split_data_indexes))
+        iid_indexes = setdiff1d(
+            torch.tensor(range(len(labels))).cuda(), torch.cat(split_data_indexes)
+        )
     else:
-        iid_indexes = setdiff1d(torch.tensor(range(len(labels))), torch.cat(split_data_indexes))
+        iid_indexes = setdiff1d(
+            torch.tensor(range(len(labels))), torch.cat(split_data_indexes)
+        )
     iid_indexes = iid_indexes[np.random.permutation(len(iid_indexes))]
 
-    for i in range(num_clients):  #for iid
-        label_i= i // average_device_of_class
+    for i in range(num_clients):  # for iid
+        label_i = i // average_device_of_class
         labels_class = shuffle_labels[label_i]
 
-        average_num= int(len(labels_class)//average_device_of_class * (1 - non_iid_percent))
-        split_data_indexes[i] = list(split_data_indexes[i]) + list(iid_indexes[:average_num])
+        average_num = int(
+            len(labels_class) // average_device_of_class * (1 - non_iid_percent)
+        )
+        split_data_indexes[i] = list(split_data_indexes[i]) + list(
+            iid_indexes[:average_num]
+        )
 
         iid_indexes = iid_indexes[average_num:]
     return split_data_indexes
 
-def get_in_comm_indexes(edge_index: torch.Tensor, split_data_indexes: list, num_clients: int, L_hop: int, idx_train: torch.Tensor, idx_test: torch.Tensor) -> tuple:
-    '''
+
+def get_in_comm_indexes(
+    edge_index: torch.Tensor,
+    split_data_indexes: list,
+    num_clients: int,
+    L_hop: int,
+    idx_train: torch.Tensor,
+    idx_test: torch.Tensor,
+) -> tuple:
+    """
     This function is used to extract and preprocess data indices and edge information
 
     Arguments:
@@ -149,90 +193,153 @@ def get_in_comm_indexes(edge_index: torch.Tensor, split_data_indexes: list, num_
     communicate_indexes: (list) - A list of indices assigned to a particular client
     in_com_train_data_indexes: (list) - A list of tensors where each tensor contains the indices of training data points available to each client
     edge_indexes_clients: (list) - A list of edge tensors representing the edges between nodes within each client's subgraph
-    '''
+    """
     communicate_indexes = []
     in_com_train_data_indexes = []
     edge_indexes_clients = []
-    
-    for i in range(num_clients):        
+
+    for i in range(num_clients):
         communicate_index = split_data_indexes[i]
-        
+
         if L_hop == 0:
-            communicate_index, current_edge_index, _, __ = torch_geometric.utils.k_hop_subgraph(communicate_index,0,edge_index, relabel_nodes=True)
+            (
+                communicate_index,
+                current_edge_index,
+                _,
+                __,
+            ) = torch_geometric.utils.k_hop_subgraph(
+                communicate_index, 0, edge_index, relabel_nodes=True
+            )
             del _
             del __
 
         for hop in range(L_hop):
-            if hop != L_hop-1:
-                communicate_index = torch_geometric.utils.k_hop_subgraph(communicate_index,1,edge_index, relabel_nodes=True)[0]
+            if hop != L_hop - 1:
+                communicate_index = torch_geometric.utils.k_hop_subgraph(
+                    communicate_index, 1, edge_index, relabel_nodes=True
+                )[0]
             else:
-                communicate_index, current_edge_index, _, __ = torch_geometric.utils.k_hop_subgraph(communicate_index,1,edge_index, relabel_nodes=True)
+                (
+                    communicate_index,
+                    current_edge_index,
+                    _,
+                    __,
+                ) = torch_geometric.utils.k_hop_subgraph(
+                    communicate_index, 1, edge_index, relabel_nodes=True
+                )
                 del _
                 del __
 
-        communicate_index = communicate_index.to('cpu')
-        current_edge_index = current_edge_index.to('cpu')
+        communicate_index = communicate_index.to("cpu")
+        current_edge_index = current_edge_index.to("cpu")
         communicate_indexes.append(communicate_index)
-        
-        current_edge_index = torch_sparse.SparseTensor(row=current_edge_index[0], col=current_edge_index[1], sparse_sizes=(len(communicate_index), len(communicate_index)))
-        
-        edge_indexes_clients.append(current_edge_index)
 
-        inter = intersect1d(split_data_indexes[i], idx_train)  ###only count the train data of nodes in current server(not communicate nodes)
+        current_edge_index = torch_sparse.SparseTensor(
+            row=current_edge_index[0],
+            col=current_edge_index[1],
+            sparse_sizes=(len(communicate_index), len(communicate_index)),
+        )
 
+        edge_indexes_clients.append(current_edge_index)
 
-        in_com_train_data_indexes.append(torch.searchsorted(communicate_indexes[i], inter).clone())   #local id in block matrix
+        inter = intersect1d(
+            split_data_indexes[i], idx_train
+        )  ###only count the train data of nodes in current server(not communicate nodes)
+
+        in_com_train_data_indexes.append(
+            torch.searchsorted(communicate_indexes[i], inter).clone()
+        )  # local id in block matrix
 
     in_com_test_data_indexes = []
     for i in range(num_clients):
-        inter = intersect1d(split_data_indexes[i], idx_test)  
-        in_com_test_data_indexes.append(torch.searchsorted(communicate_indexes[i], inter).clone())   
-    return communicate_indexes, in_com_train_data_indexes, in_com_test_data_indexes, edge_indexes_clients
-
-
-def get_in_comm_indexes_BDS_GCN(edge_index: torch.Tensor, split_data_indexes: list, num_clients: int, idx_train: torch.Tensor, idx_test: torch.Tensor, sample_rate: float = 0.5) -> tuple:
+        inter = intersect1d(split_data_indexes[i], idx_test)
+        in_com_test_data_indexes.append(
+            torch.searchsorted(communicate_indexes[i], inter).clone()
+        )
+    return (
+        communicate_indexes,
+        in_com_train_data_indexes,
+        in_com_test_data_indexes,
+        edge_indexes_clients,
+    )
+
+
+def get_in_comm_indexes_BDS_GCN(
+    edge_index: torch.Tensor,
+    split_data_indexes: list,
+    num_clients: int,
+    idx_train: torch.Tensor,
+    idx_test: torch.Tensor,
+    sample_rate: float = 0.5,
+) -> tuple:
     communicate_indexes = []
     in_com_train_data_indexes = []
     edge_indexes_clients = []
-    
-    for i in range(num_clients):        
+
+    for i in range(num_clients):
         communicate_index = split_data_indexes[i]
-        
-        communicate_index = torch_geometric.utils.k_hop_subgraph(communicate_index,1,edge_index)[0].cpu()
-        
+
+        communicate_index = torch_geometric.utils.k_hop_subgraph(
+            communicate_index, 1, edge_index
+        )[0].cpu()
+
         diff = setdiff1d(split_data_indexes[i], communicate_index)
-        sample_index = torch.cat((split_data_indexes[i], diff[torch.randperm(len(diff))[:int(len(diff) * sample_rate)]])).clone()
+        sample_index = torch.cat(
+            (
+                split_data_indexes[i],
+                diff[torch.randperm(len(diff))[: int(len(diff) * sample_rate)]],
+            )
+        ).clone()
         sample_index = sample_index.sort()[0]
-        
-        #get edge_index with relabel_nodes
-        communicate_index, current_edge_index, _, __ = torch_geometric.utils.k_hop_subgraph(sample_index,0,edge_index, relabel_nodes=True)
+
+        # get edge_index with relabel_nodes
+        (
+            communicate_index,
+            current_edge_index,
+            _,
+            __,
+        ) = torch_geometric.utils.k_hop_subgraph(
+            sample_index, 0, edge_index, relabel_nodes=True
+        )
         del _
         del __
 
-        communicate_index = communicate_index.to('cpu')
-        current_edge_index = current_edge_index.to('cpu')
+        communicate_index = communicate_index.to("cpu")
+        current_edge_index = current_edge_index.to("cpu")
         communicate_indexes.append(communicate_index)
-        
-        current_edge_index = torch_sparse.SparseTensor(row=current_edge_index[0], col=current_edge_index[1], sparse_sizes=(len(communicate_index), len(communicate_index)))
-        
-        edge_indexes_clients.append(current_edge_index)
-
-        inter = intersect1d(split_data_indexes[i], idx_train)  ###only count the train data of nodes in current server(not communicate nodes)
 
+        current_edge_index = torch_sparse.SparseTensor(
+            row=current_edge_index[0],
+            col=current_edge_index[1],
+            sparse_sizes=(len(communicate_index), len(communicate_index)),
+        )
 
-        in_com_train_data_indexes.append(torch.searchsorted(communicate_indexes[i], inter).clone())   #local id in block matrix
+        edge_indexes_clients.append(current_edge_index)
 
+        inter = intersect1d(
+            split_data_indexes[i], idx_train
+        )  ###only count the train data of nodes in current server(not communicate nodes)
 
+        in_com_train_data_indexes.append(
+            torch.searchsorted(communicate_indexes[i], inter).clone()
+        )  # local id in block matrix
 
     in_com_test_data_indexes = []
     for i in range(num_clients):
-        inter = intersect1d(split_data_indexes[i], idx_test)  
-        in_com_test_data_indexes.append(torch.searchsorted(communicate_indexes[i], inter).clone())   
-    return communicate_indexes, in_com_train_data_indexes, in_com_test_data_indexes, edge_indexes_clients
-
-
-def increment_dir(dir: str, comment: str ='') -> str:
-    '''
+        inter = intersect1d(split_data_indexes[i], idx_test)
+        in_com_test_data_indexes.append(
+            torch.searchsorted(communicate_indexes[i], inter).clone()
+        )
+    return (
+        communicate_indexes,
+        in_com_train_data_indexes,
+        in_com_test_data_indexes,
+        edge_indexes_clients,
+    )
+
+
+def increment_dir(dir: str, comment: str = "") -> str:
+    """
     This function is used to create a new directory path by incrementing a numeric suffix in the original directory path
 
     Arguments:
@@ -242,14 +349,14 @@ def increment_dir(dir: str, comment: str ='') -> str:
     Returns:
     Returns a string with the path of the new directory
 
-    '''
+    """
     # Increments a directory runs/exp1 --> runs/exp2_comment
     n = 0  # number
     dir = str(Path(dir))  # os-agnostic
-    dirs = sorted(glob.glob(dir + '*'))  # directories
+    dirs = sorted(glob.glob(dir + "*"))  # directories
     if dirs:
         matches = [re.search(r"exp(\d+)", d) for d in dirs]
         idxs = [int(m.groups()[0]) for m in matches if m]
         if idxs:
             n = max(idxs) + 1  # increment
-    return dir + str(n) + ('_' + comment if comment else '')
+    return dir + str(n) + ("_" + comment if comment else "")