Natl Sci Open
Volume 2, Number 1, 2023
|Number of page(s)||17|
|Published online||10 January 2023|
DeceFL: a principled fully decentralized federated learning framework
1 School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
2 School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
3 Department of Applied Mathematics, University of Waterloo, Waterloo N2L 3G1, Canada
4 State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China
5 School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan 430200, China
6 School of Electrical Engineering and Computer Science, and Digital Futures, KTH Royal Institute of Technology, Stockholm 10044, Sweden
7 Shenyang Institute of Automation (SIA), Chinese Academy of Sciences, Shenyang 110169, China
8 School of Mathematics, Frontiers Science Center for Mobile Information Communication and Security, Southeast University, Nanjing 211189, China
9 Purple Mountain Laboratories, Nanjing 211111, China
10 AVIC Chengdu Aircraft Industrial (Group) Co., Ltd., Chengdu 610091, China
* Corresponding author (email: firstname.lastname@example.org)
Revised: 5 October 2022
Accepted: 28 October 2022
Traditional machine learning relies on a centralized data pipeline for model training in various applications; however, data are inherently fragmented. Such a decentralized nature of databases presents the serious challenge for collaboration: sending all decentralized datasets to a central server raises serious privacy concerns. Although there has been a joint effort in tackling such a critical issue by proposing privacy-preserving machine learning frameworks, such as federated learning, most state-of-the-art frameworks are built still in a centralized way, in which a central client is needed for collecting and distributing model information (instead of data itself) from every other client, leading to high communication burden and high vulnerability when there exists a failure at or an attack on the central client. Here we propose a principled decentralized federated learning algorithm (DeceFL), which does not require a central client and relies only on local information transmission between clients and their neighbors, representing a fully decentralized learning framework. It has been further proven that every client reaches the global minimum with zero performance gap and achieves the same convergence rate O(1/T) (where T is the number of iterations in gradient descent) as centralized federated learning when the loss function is smooth and strongly convex. Finally, the proposed algorithm has been applied to a number of applications to illustrate its effectiveness for both convex and nonconvex loss functions, time-invariant and time-varying topologies, as well as IID and Non-IID of datasets, demonstrating its applicability to a wide range of real-world medical and industrial applications.
Key words: decentralized federated learning / smart manufacturing / control systems privacy
© The Author(s) 2023. Published by China Science Publishing & Media Ltd. and EDP Sciences.
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