Add README.md for dp, mf and optimization

scripts/README.md

@@ -2,10 +2,43 @@
 We provide some scripts for reproducing existing algorithms with FederatedScope, which are constantly being updated.
 We greatly appreciate any [contribution](https://federatedscope.io/docs/contributor/) to FederatedScope!
 
+- [Federated Optimization Algorithms](#fed-optimization)
 - [Distribute Mode](#distribute-mode)
 - [Asynchronous Training Strategy](#asynchronous-training-strategy)
 - [Graph Federated Learning](#graph-federated-learning)
 - [Attacks in Federated Learning](#attacks-in-FL)
+- [Differential Privacy in Federated Learning](#dp-in-FL)
+- [Matrix Factorization in Federated Learning](#mf-in-FL)
+
+### Federated Optimization Algorithm
+Users can replace the fedavg algorithm by other federated optimization algorithms.
+In the following we provide some running scripts for FedOpt[1] and FedProx[2] on different dataset.
+
+#### FedOpt
+Run fedopt on different dataset via
+```bash
+# on femnist
+bash optimization_exp_scripts/fedopt_exp_scripts/run_fedopt_femnist.sh
+# on synthetic
+bash optimization_exp_scripts/fedopt_exp_scripts/run_fedopt_lr.sh
+# on shakespeare
+bash optimization_exp_scripts/fedopt_exp_scripts/run_fedopt_shakespeare.sh
+```
+
+#### FedProx
+Run fedprox on different dataset via
+```bash
+# on femnist
+bash optimization_exp_scripts/fedprox_exp_scripts/run_fedprox_femnist.sh
+# on lr
+bash optimization_exp_scripts/fedprox_exp_scripts/run_fedprox_lr.sh
+# on shakespeare
+bash optimization_exp_scripts/fedprox_exp_scripts/run_fedprox_shakespeare.sh
+```
+
+[1] Asad M, Moustafa A, Ito T. "FedOpt: Towards communication efficiency and privacy preservation in federated learning". Applied Sciences, 2020, 10(8): 2864.
+
+[2] Anit Kumar Sahu, Tian Li, Maziar Sanjabi, Manzil Zaheer, Ameet Talwalkar, Virginia Smith. "On the Convergence of Federated Optimization in Heterogeneous Networks." ArXiv abs/1812.06127 (2018).
 
 ### Distribute Mode
 Users can train an LR on generated toy data with distribute mode via:
@@ -98,4 +131,40 @@ Run the BadNet attack:
 python federatedscope/main.py --cfg scripts/attack_exp_scripts/backdoor_attack/backdoor_badnet_fedavg_convnet2_on_femnist.yaml
 ```
 
+### Differential Privacy in Federated Learning
+
+Users can train models with protection of differential privacy. 
+Taking the dataset FEMNIST as an example, execute the running scripts via:
+```bash
+bash dp_exp_scripts/run_femnist_dp_standalone.sh
+```
+You can also enable DP algorithm with other dataset and models by adding the following configurations:
+```yaml
+nbafl: 
+  use: True
+  mu: 0.1
+  epsilon: 10
+  constant: 30
+  w_clip: 0.1
+federate:
+  join_in_info: ["num_sample"]
+```
+
+### Matrix Factorization in Federated Learning
+We support federated matrix factorization tasks in both vertical and horizontal federated learning scenario. 
+Users can run matrix factorization tasks on MovieLen dataset via
+```bash
+# vfl
+bash mf_exp_scripts/run_movielens1m_vfl_standalone.sh
+# hfl
+bash mf_exp_scripts/run_movielens1m_hfl_standalone.sh
+```
+Also, we support SGDMF[1] algorithm in federated learning, and users can run it via
+```bash
+# hfl
+bash mf_exp_scripts/run_movielens1m_hflsgdmf_standalone.sh
+# vfl
+bash mf_exp_scripts/run_movielens1m_vflsgdmf_standalone.sh
+```
 
+[1] Zitao Li, Bolin Ding, Ce Zhang, Ninghui Li, Jingren Zhou. "Federated Matrix Factorization with Privacy Guarantee." Proceedings of the VLDB Endowment, 15(4): 900-913 (2021).

scripts/dp_exp_scripts/run_femnist_dp_standalone.sh

@@ -1,54 +1,13 @@
 set -e
 
-cudaid=$1
+cd ..
 
-if [ ! -d "out_nbafl" ];then
-  mkdir out_nbafl
-fi
-
-echo "NbAFL starts..."
-
-clips=(0.1)
-epsilons=(10. 50. 100.)
-mus=(0.01)
-constants=(1. 2. 3.)
-
-for ((iw=0; iw<${#clips[@]}; iw++ ))
-do
-    for ((ie=0; ie<${#epsilons[@]}; ie++ ))
-    do
-        for ((im=0; im<${#mus[@]}; im++ ))
-        do
-            for ((ic=0; ic<${#constants[@]}; ic++ ))
-            do
-                python federatedscope/main.py --cfg federatedscope/cv/baseline/fedavg_convnet2_on_femnist.yaml device ${cudaid} nbafl.use True \
-                data.root /mnt/gaodawei.gdw/data/ \
-                nbafl.mu ${mus[$im]} \
-                nbafl.epsilon ${epsilons[$ie]} \
-                nbafl.constant ${constants[$ic]} \
-                nbafl.w_clip ${clips[$iw]} \
-                >>out_nbafl/temp.out \
-                2>>out_nbafl/clip_${clips[$iw]}_eps_${epsilons[$ie]}_mu_${mus[$im]}_const_${constants[$ic]}.log
-            done
-        done
-    done
-done
-
-for ((iw=0; iw<${#clips[@]}; iw++ ))
-do
-    for ((ie=0; ie<${#epsilons[@]}; ie++ ))
-    do
-        for ((im=0; im<${#mus[@]}; im++ ))
-        do
-            for ((ic=0; ic<${#constants[@]}; ic++ ))
-            do
-                python federatedscope/../scripts/dp_exp_scripts/parse_nbafl_results.py --input out_nbafl/clip_${clips[$iw]}_eps_${epsilons[$ie]}_mu_${mus[$im]}_const_${constants[$ic]}.log \
-                --round 300\
-                >>out_nbafl/parse.log
-            done
-        done
-    done
-done
-
-echo "Ends."
+echo "Run NbAFL on femnist."
 
+python federatedscope/main.py --cfg federatedscope/cv/baseline/fedavg_convnet2_on_femnist.yaml\
+                nbafl.use True \
+                nbafl.mu 0.1 \
+                nbafl.epsilon 20. \
+                nbafl.constant 1. \
+                nbafl.w_clip 0.1 \
+                federate.join_in_info ["num_sample"]