Web: http://arxiv.org/abs/2204.05011

May 4, 2022, 1:12 a.m. | Yuexiang Xie, Zhen Wang, Daoyuan Chen, Dawei Gao, Liuyi Yao, Weirui Kuang, Yaliang Li, Bolin Ding, Jingren Zhou

cs.LG updates on arXiv.org arxiv.org

Although remarkable progress has been made by the existing federated learning
(FL) platforms to provide fundamental functionalities for development, these
platforms cannot well tackle the challenges brought by the heterogeneity of FL
scenarios from both academia and industry. To fill this gap, in this paper, we
propose a flexible federated learning platform, named FederatedScope, for
handling various types of heterogeneity in FL. Considering both flexibility and
extendability, FederatedScope adopts an event-driven architecture to
conveniently support asynchronous training protocol in practical …

arxiv federated learning learning platform

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