Federated learning (FL) is a rapidly growing research field in machine learning. However, existing FL libraries cannot adequately support diverse algorithmic development; inconsistent dataset and model usage make fair algorithm comparison challenging. In this work, we introduce FedML, an open research library and benchmark to facilitate FL algorithm development and fair performance comparison. FedML supports three computing paradigms: on-device training for edge devices, distributed computing, and single-machine simulation. FedML also promotes diverse algorithmic research with flexible and generic API design and comprehensive reference baseline implementations (optimizer, models, and datasets). We hope FedML could provide an efficient and reproducible means for developing and evaluating FL algorithms that would benefit the FL research community. We maintain the source code, documents, and user community at https://fedml.ai.
@article{arxiv.2007.13518,
title = {FedML: A Research Library and Benchmark for Federated Machine Learning},
author = {Chaoyang He and Songze Li and Jinhyun So and Xiao Zeng and Mi Zhang and Hongyi Wang and Xiaoyang Wang and Praneeth Vepakomma and Abhishek Singh and Hang Qiu and Xinghua Zhu and Jianzong Wang and Li Shen and Peilin Zhao and Yan Kang and Yang Liu and Ramesh Raskar and Qiang Yang and Murali Annavaram and Salman Avestimehr},
journal= {arXiv preprint arXiv:2007.13518},
year = {2020}
}
Comments
This is FedML white paper V3. Homepage: https://fedml.ai; GitHub: https://github.com/FedML-AI/FedML; In V3, More advanced algorithms and IoT device training are supported, please check here: https://github.com/FedML-AI/FedML/blob/master/fedml_iot/