bugfix for scripts

README.md

@@ -163,7 +163,7 @@ Here we demonstrate how to run a standard FL task with FederatedScope, with sett
 # Run with default configurations
 python federatedscope/main.py --cfg scripts/example_configs/femnist.yaml
 # Or with custom configurations
-python federatedscope/main.py --cfg scripts/example_configs/femnist.yaml federated.total_round_num 50 data.batch_size 128
+python federatedscope/main.py --cfg scripts/example_configs/femnist.yaml federate.total_round_num 50 data.batch_size 128
 ```
 
 Then you can observe some monitored metrics during the training process as:
@@ -214,7 +214,7 @@ python federatedscope/main.py --cfg scripts/distributed_scripts/distributed_conf
 An executable example with generated toy data can be run with (a script can be found in `scripts/run_distributed_lr.sh`):
 ```bash
 # Generate the toy data
-python scripts/gen_data.py
+python scripts/distributed_scripts/gen_data.py
 
 # Firstly start the server that is waiting for clients to join in
 python federatedscope/main.py --cfg scripts/distributed_scripts/distributed_configs/distributed_server.yaml distribute.data_file toy_data/server_data distribute.server_host 127.0.0.1 distribute.server_port 50051

federatedscope/core/workers/server.py

@@ -572,7 +572,7 @@ def merge_eval_results_from_all_clients(self):
                             float(client_eval_results[key]))
                 formatted_logs = self._monitor.format_eval_res(
                     metrics_all_clients,
-                    rnd=self.state,
+                    rnd=round,
                     role='Server #',
                     forms=self._cfg.eval.report)
                 if merge_type == "unseen":

scripts/README.md

@@ -5,7 +5,7 @@ We greatly appreciate any [contribution](https://federatedscope.io/docs/contribu
 - [Distribute Mode](#distribute-mode)
 - [Asynchronous Training Strategy](#asynchronous-training-strategy)
 - [Graph Federated Learning](#graph-federated-learning)
-- [Attacks in Federated Learning](#attacks-in-FL)
+- [Attacks in Federated Learning](#attacks-in-federated-learning)
 
 ### Distribute Mode
 Users can train an LR on generated toy data with distribute mode via:
@@ -50,11 +50,13 @@ python federatedscope/main.py --cfg federatedscope/cv/baseline/fedbn_convnet2_on
 We provide an example for training ConvNet on CIFAR-10 with asynchronous training strategies:
 ```shell script
 cd ..
-python federatedscope/main.py --cfg scritpes/example_configs/asyn_cifar10.yaml
+python federatedscope/main.py --cfg scripts/example_configs/asyn_cifar10.yaml
 ```
 The FL courses consists of 1 server and 200 clients, which applies `goal_achieved` strategies and set the `min_received_num=10` and `staleness_toleration=10`.
 Users can change the configurations related to asynchronous training for customization. Please see [configurations](https://github.com/alibaba/FederatedScope/tree/master/federatedscope/core/configs).
 
+Note that users can manually download [cifar-10](https://www.cs.toronto.edu/~kriz/cifar.html) dataset and put it to `FederatedScope/data` if the automatic download process failed. And for `resource_info_file`, we take the [client_device_capacity](https://github.com/SymbioticLab/FedScale/blob/master/benchmark/dataset/data/device_info/client_device_capacity) provided by [1] as an example.
+
 ### Graph Federated Learning
 Please refer to [gfl](https://github.com/alibaba/FederatedScope/tree/master/federatedscope/gfl) for more details.
 
@@ -65,37 +67,41 @@ We provide the following four examples to run the membership inference attack, p
 
 Membership inference attack:
 
-Run the attack in [1]:
+Run the attack in [2]:
 ```shell script
 python federatedscope/main.py --cfg scripts/attack_exp_scripts/privacy_attack/gradient_ascent_MIA_on_femnist.yaml
 ```
 
-Property inference attack: Run the BPC [1] attack
+Property inference attack: Run the BPC [2] attack
 ```shell script
 python federatedscope/main.py --cfg scripts/attack_exp_scripts/privacy_attack/PIA_toy.yaml
 ```
 
-Class representative attack: Run DCGAN [2] attack
+Class representative attack: Run DCGAN [3] attack
 ```shell script
 python federatedscope/main.py --cfg scripts/attack_exp_scripts/privacy_attack/CRA_fedavg_convnet2_on_femnist.yaml
 ```
 
-Training data/label inference attack: Run the DLG [3] attack 
+Training data/label inference attack: Run the DLG [4] attack 
 ```shell script
 python federatedscope/main.py --cfg scripts/attack_exp_scripts/privacy_attack/reconstruct_fedavg_opt_on_femnist.yaml
 ```
 
-[1] Nasr, Milad, R. Shokri and Amir Houmansadr. “Comprehensive Privacy Analysis of Deep Learning: Stand-alone and Federated Learning under Passive and Active White-box Inference Attacks.” ArXiv abs/1812.00910 (2018): n. pag.
-
-[2] Hitaj, Briland, Giuseppe Ateniese, and Fernando Perez-Cruz. "Deep models under the GAN: information leakage from collaborative deep learning." Proceedings of the 2017 ACM SIGSAC conference on computer and communications security. 2017
-
-[3] Zhu, Ligeng, Zhijian Liu, and Song Han. "Deep leakage from gradients." Advances in Neural Information Processing Systems 32 (2019).
 
 #### Backdoor Attacks
 
-Run the BadNet attack:
+Run the BadNets [5] attack:
 ```shell script
 python federatedscope/main.py --cfg scripts/attack_exp_scripts/backdoor_attack/backdoor_badnet_fedavg_convnet2_on_femnist.yaml
 ```
 
+### References:  
+[1] Lai F, Dai Y, Singapuram S, et al. "FedScale: Benchmarking model and system performance of federated learning at scale." International Conference on Machine Learning. PMLR, 2022: 11814-11827.
+
+[2] Nasr, Milad, R. Shokri and Amir Houmansadr. "Comprehensive Privacy Analysis of Deep Learning: Stand-alone and Federated Learning under Passive and Active White-box Inference Attacks." ArXiv abs/1812.00910 (2018).
+
+[3] Hitaj, Briland, Giuseppe Ateniese, and Fernando Perez-Cruz. "Deep models under the GAN: information leakage from collaborative deep learning." Proceedings of the 2017 ACM SIGSAC conference on computer and communications security.
+
+[4] Zhu, Ligeng, Zhijian Liu, and Song Han. "Deep leakage from gradients." Advances in Neural Information Processing Systems 32 (2019).
 
+[5] Tianyu Gu, Kang Liu, Brendan Dolan-Gavitt, and Siddharth Garg. 2019. "BadNets: Evaluating Backdooring Attacks on Deep Neural Networks." IEEE Access 7 (2019), 47230-47244.

scripts/attack_exp_scripts/privacy_attack/PIA_toy.yaml

@@ -1,4 +1,4 @@
-use_gpu: True
+use_gpu: False
 device: 0
 seed: 12345
 federate:

scripts/distributed_scripts/run_distributed_lr.sh

@@ -5,7 +5,7 @@ cd ..
 echo "Test distributed mode with LR..."
 
 echo "Data generation"
-python scripts/gen_data.py
+python scripts/distributed_scripts/gen_data.py
 
 ### server owns global test data
 python federatedscope/main.py --cfg scripts/distributed_scripts/distributed_configs/distributed_server.yaml &

scripts/example_configs/asyn_cifar10.yaml

@@ -15,7 +15,7 @@ federate:
 data:
   root: data/
   type: CIFAR10@torchvision
-  args: [{'download': False}]
+  args: [{'download': True}]
   splits: [0.8,0.2,0.2]
   batch_size: 10
   subsample: 0.2