liuyuqinggg / fedlab

A flexible Federated Learning Framework based on PyTorch, simplifying your Federated Learning research.

FedLab: A Flexible Federated Learning Framework

Federated learning (FL), proposed by Google at the very beginning, is recently a burgeoning research area of machine learning, which aims to protect individual data privacy in distributed machine learning process, especially in finance, smart healthcare and edge computing. Different from traditional data-centered distributed machine learning, participants in FL setting utilize localized data to train local model, then leverages specific strategies with other participants to acquire the final model collaboratively, avoiding direct data sharing behavior.

To relieve the burden of researchers in implementing FL algorithms and emancipate FL scientists from repetitive implementation of basic FL setting, we introduce highly customizable framework FedLab in this work. FedLab provides the necessary modules for FL simulation, including communication, compression, model optimization, data partition and other functional modules. Users can build FL simulation environment with custom modules like playing with LEGO bricks. For better understanding and easy usage, FL algorithm benchmark implemented in FedLab are also presented.

Content of Documentations

• Documentation website
• Overview of FedLab
• Installation & Setup
• Examples
• Contribute Guideline
• API Reference

Quick start

1. Please read our tutorials in jupyter notebook.

2. Run our quick start examples of different scenarios with partitioned MNIST dataset.

# example of standalone
$ cd ./examples/standalone/
$ bash python standalone.py --total_client 100 --com_round 3 --sample_ratio 0.1 --batch_size 100 --epochs 5 --lr 0.02

Baselines

We provide the reproduction of baseline federated algorthms for users in this repo.

Method	Type	Paper	Publication	Official code
FedAvg	Optim.	Communication-Efficient Learning of Deep Networks from Decentralized Data	AISTATS'2017
FedProx	Optim.	Federated Optimization in Heterogeneous Networks	MLSys' 2020	Code
FedDyn	Optim.	Federated Learning Based on Dynamic Regularization	ICLR' 2021	Code
q-FFL	Optim.	Fair Resource Allocation in Federated Learning	ICLR' 2020	Code
FedNova	Optim.	Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization	NeurIPS'2020	Code
IFCA	Optim.	An Efficient Framework for Clustered Federated Learning	NeurIPS'2020	Code
Ditto	Optim.	Ditto: Fair and Robust Federated Learning Through Personalization	ICML'2021	Code
Power-of-choice		Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies	Pre-print
SCAFFOLD	Optim.	SCAFFOLD: Stochastic Controlled Averaging for Federated Learning	ICML'2020
Personalized-FedAvg		Improving Federated Learning Personalization via Model Agnostic Meta Learning
QSGD	Com	QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding	NeurIPS'2017
NIID-Bench	Data.	Federated Learning on Non-IID Data Silos: An Experimental Study	Pre-print	Code
LEAF	Data.	LEAF: A Benchmark for Federated Settings	Pre-print	Code

Performance

TODO

Data Partition

TODO

Citation

Please cite FedLab in your publications if it helps your research:

@article{smile2021fedlab,  
    title={FedLab: A Flexible Federated Learning Framework},  
    author={Dun Zeng, Siqi Liang, Xiangjing Hu, Hui Wang and Zenglin Xu},  
    journal={arXiv preprint arXiv:2107.11621},  
    year={2021}
}

Contact

Project Investigator: Prof. Zenglin Xu ([email protected]).

For technical issues reated to FedLab development, please contact our development team through Github issues or email:

• Dun Zeng: [email protected]
• Siqi Liang: [email protected]