FederatedScope: A Comprehensive and Flexible Federated Learning Platform
via Message Passing
- URL: http://arxiv.org/abs/2204.05011v1
- Date: Mon, 11 Apr 2022 11:24:21 GMT
- Title: FederatedScope: A Comprehensive and Flexible Federated Learning Platform
via Message Passing
- Authors: Yuexiang Xie, Zhen Wang, Daoyuan Chen, Dawei Gao, Liuyi Yao, Weirui
Kuang, Yaliang Li, Bolin Ding, Jingren Zhou
- Abstract summary: We propose a novel and comprehensive federated learning platform, named FederatedScope, which is based on a message-oriented framework.
Compared to the procedural framework, the proposed message-oriented framework is more flexible to express heterogeneous message exchange.
We conduct a series of experiments on the provided easy-to-use and comprehensive FL benchmarks to validate the correctness and efficiency of FederatedScope.
- Score: 63.87056362712879
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Although remarkable progress has been made by the existing federated learning
(FL) platforms to provide fundamental functionalities for development, these FL
platforms cannot well satisfy burgeoning demands from rapidly growing FL tasks
in both academia and industry. To fill this gap, in this paper, we propose a
novel and comprehensive federated learning platform, named FederatedScope,
which is based on a message-oriented framework. Towards more handy and flexible
support for various FL tasks, FederatedScope frames an FL course into several
rounds of message passing among participants, and allows developers to
customize new types of exchanged messages and the corresponding handlers for
various FL applications. Compared to the procedural framework, the proposed
message-oriented framework is more flexible to express heterogeneous message
exchange and the rich behaviors of participants, and provides a unified view
for both simulation and deployment. Besides, we also include several functional
components in FederatedScope, such as personalization, auto-tuning, and privacy
protection, to satisfy the requirements of frontier studies in FL. We conduct a
series of experiments on the provided easy-to-use and comprehensive FL
benchmarks to validate the correctness and efficiency of FederatedScope. We
have released FederatedScope for users on
https://github.com/alibaba/FederatedScope to promote research and industrial
deployment of federated learning in a variety of real-world applications.
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