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arXiv · 2007.13518

FedML: A Research Library and Benchmark for Federated Machine Learning

Chaoyang He·Songze Li·Jinhyun So·Xiao Zeng·Mi Zhang·Hongyi Wang·Xiaoyang Wang·Praneeth Vepakomma·Abhishek Singh·Hang Qiu·Xinghua Zhu·Jianzong Wang·Li Shen·Peilin Zhao·Yan Kang·Yang Liu·Ramesh Raskar·Qiang Yang·Murali Annavaram·Salman Avestimehr
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Abstract

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.

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Chaoyang He, Songze Li, Jinhyun So, Xiao Zeng, Mi Zhang, Hongyi Wang, Xiaoyang Wang, Praneeth Vepakomma, Abhishek Singh, Hang Qiu, Xinghua Zhu, Jianzong Wang, Li Shen, Peilin Zhao, Yan Kang, Yang Liu, Ramesh Raskar, Qiang Yang, Murali Annavaram, Salman Avestimehr. 2020-07-27. FedML: A Research Library and Benchmark for Federated Machine Learning. https://arxiv.org/abs/2007.13518

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