The source code of the Arxiv preprint article (FlexCFL):

Flexible Clustered Federated Learning for Client-Level Data Distribution Shift

This is the extended journal version of our previous FedGroup conference paper.

🎉FlexCFL is a wholly new reconstruction of our previous CFL framework FedGroup.

There are many exciting improvements of FlexCFL:

• TF2.0 support. (FedGroup uses tensorflow.compat.v1 API)
• Run faster and reading friendly. (Previous FedGroup is based on FedProx)
• Easy to get started with only a few lines of configuration.
• The output is saved in excel format.

New functions of FlexCFL:

• Simulation of client-level distribution shift.
• Client migration strategy.
• Evaluation for auxiliary server model (global average model).
• Temperature aggregation (experimental).

Some technical fixes of FlexCFL:

• The aggregation strategy of IFCA and FeSEM change to simply averaging according to the original description.
• The maximum accuracy does not include the 'partial accuracy' (In the early training period, not all clients participate in the test)
• Cold start client gradually.

FlexCFL can simulate following (Clustered) Federated Learning frameworks:

• FedAvg & FedSGD -> Communication-Efficient Learning of Deep Networks from Decentralized Data
• FedGrop & FedGroup-RAC & FedGroup-RCC -> FedGroup: Efficient Clustered Federated Learning via Decomposed Data-Driven Measure
• IFCA -> An Efficient Framework for Clustered Federated Learning
• FeSEM -> Multi-center federated learning
• FlexCFL & FlexCFL with group aggregation -> Flexible Clustered Federated Learning for Client-Level Data Distribution Shift

Python packages:

• Tensorflow (>2.0)
• Jupyter Notebook
• scikit-learn
• matplotlib
• tqdm

You need to download the dataset (e.g. FEMNIST, MNIST, FashionMNIST, Synthetic) and specify a GPU id follows the guidelines of FedProx & Ditto.

The directory structure of the datasets should look like this:

FlexCFL-->data-->mnist-->data-->train--> ***train.json
                |              |->test--> ***test.json
                |
                |->femnist-->data-->train--> ***train.json
                |                  |->test--> ***test.json
                |
                |->fmnist-->data-->...
                |
                |->synthetic_1_1-->data-->...
                |
                ...

Just run test.ipynb. The task_list shows examples of several configurations. The default configurations are defined in FlexCFL/utils/trainer_utils.py as TrainConfig.

You can modify the configurations by directly modifying the config of trainer. The commonly used hyperparameters of FlexCFL are:

# The dataset name, data file should be stored in floder FLexCFL/data/
trainer_config['dataset'] = 'femnist'

# The model name, model definition file should be saved in floder FLexCFL/flearn/model/
trainer_config['model'] = 'mlp'

# Total communication round
trainer_config['num_rounds'] = 300

# Evalution interval round
trainer_config['eval_every'] = 1

# Number of group
trainer_config['num_group'] = 5

# Evalute the global average model
trainer_config['eval_global_model'] = True

# Inter-group aggregation rate
trainer_config['group_agg_lr'] = 0.1

# Pretraining scale for group cold start of FlexCFL
trainer_config['pretrain_scale'] = 20

# Client data distribution shift config
trainer_config['shift_type'] = 'all'
trainer_config['swap_p'] = 0.05

# Client migration strategy
trainer_config['dynamic'] = True

# The local epoch, mini-batch size, learning rate for local SGD
client_config['local_epochs'] = 10
client_config['batch_size'] = 10
client_config['learning_rate'] = 0.003

You can also run FlexCFL with python main.py. Please modify config according to your needs.

All evaluation results will save in the FlexCFL-->results-->... directory as excel format files.

Please cite the paper of FlexCFL if the code helped your research 😊

• Flexible Clustered Federated Learning for Client-Level Data Distribution Shift

BibTeX

@article{duan2022flexible,
  title={Flexible Clustered Federated Learning for Client-Level Data Distribution Shift},
  author={Duan, Moming and Liu, Duo and Ji, Xinyuan and Wu, Yu and Liang, Liang and Chen, Xianzhang and Tan, Yujuan and Ren, Ao},
  journal={IEEE Transactions on Parallel \& Distributed Systems},
  volume={33},
  number={11},
  pages={2661--2674},
  year={2022},
  publisher={IEEE Computer Society}
}