Special Topics in Federated Learning
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 3
694-699

Efficient federated learning: norm-weighted aggregation algorithm

Chen Pana,b
Zhang Hengrua,b
Min Fana,b
a. School of Computer Science, b. Laboratory of Machine Learning, Southwest Petroleum University, Chengdu 610500, China
DOI: 10.19734/j.issn.1001-3695.2023.07.0327

Abstract

In federated learning, the non-independent and identically distributed(non-IID) data across clients leads to slower convergence of the global model and significantly increases communication costs. Existing methods collect information about the label distribution of clients to determine aggregation weights for local models, accelerating convergence, but this may leak clients' privacy. To address the slower convergence caused by non-IID data without leaking clients' privacy, this paper proposed the FedNA aggregation algorithm. FedNA achieved this goal in two ways. Firstly, it assigned aggregation weights based on the L1 norm of the class weight updates of local models to retain their contributions. Secondly, it set the class weight updates corresponding to missing classes at the clients to 0 to mitigate their impact on aggregation. Experiments were conducted under four different data distributions on two datasets. The results show that compared to FedAvg, the FedNA algorithm can reduce the number of iterations required to reach steady state by 890 at best, lowering communication costs by 44.5%. FedNA maintains clients' privacy while accelerating the convergence of the global model and decreasing communication costs. It is suitable for situations that need to protect clients privacy and are sensitive to communication efficiency.

Foundation Support

国家自然科学基金资助项目(61902328)
南充市科技局应用基础研究项目(SXHZ040,SXHZ051)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.07.0327
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 3
Section: Special Topics in Federated Learning
Pages: 694-699
Serial Number: 1001-3695(2024)03-008-0694-06

Publish History

[2023-11-01] Accepted Paper
[2024-03-05] Printed Article

Cite This Article

陈攀, 张恒汝, 闵帆. 高效联邦学习：范数加权聚合算法 [J]. 计算机应用研究, 2024, 41 (3): 694-699. (Chen Pan, Zhang Hengru, Min Fan. Efficient federated learning: norm-weighted aggregation algorithm [J]. Application Research of Computers, 2024, 41 (3): 694-699. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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