App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 7

High efficient federated learning algorithm based on Z-Score dynamic compression

Liu Qiaoshoua,b,c
Pi Shengwena,b,c
Yuan Weixia,b,c
a. School of Communications & Information Engineering, b. Advanced Network & Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China, c. Chongqing Key Laboratory of Ubiquitous Sensing & Networking, Chongqing University of Posts & Telecommunications, Chongqing 400065, China
DOI: 10.19734/j.issn.1001-3695.2023.11.0540

Abstract

Federated Learning, as an emerging distributed computing paradigm with privacy protection, safeguards user privacy and data security to a certain extent. However, in federated learning systems, the frequent exchange of model parameters between clients and servers results in significant communication overhead. In bandwidth-limited wireless communication scenarios, this has become the primary bottleneck restricting the development of federated learning. To solve this problem, this paper proposed a dynamic sparse compression algorithm based on Z-Score. By utilizing Z-Score, outlier detection is performed on local model updates, considering significant update values as outliers and subsequently selecting them. Without complex sorting algorithms or prior knowledge of the original model updates, model update sparsification is achieved. At the same time, with the increase of communication rounds, the sparse rate is dynamically adjusted according to the loss value of the global model to minimize the total traffic while ensuring the accuracy of the model. Experiments show that in the I. I. D. data scenario, the proposed algorithm can reduce communication traffic by 95% compared with the federated average algorithm, and the accuracy loss is only 1.6%. Additionally, compared with the FTTQ algorithm, the proposed algorithm can also reduce communication traffic by 40~50%, with only a 1.29% decrease in accuracy. It is proved that the method can significantly reduce the communication cost while ensuring the performance of the model.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.11.0540
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 7

Publish History

[2024-01-22] Accepted Paper

Cite This Article

刘乔寿, 皮胜文, 原炜锡. 基于Z-Score动态压缩的高效联邦学习算法 [J]. 计算机应用研究, 2024, 41 (7). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.11.0540. (Liu Qiaoshou, Pi Shengwen, Yuan Weixi. High efficient federated learning algorithm based on Z-Score dynamic compression [J]. Application Research of Computers, 2024, 41 (7). (2024-04-10). https://doi.org/10.19734/j.issn.1001-3695.2023.11.0540. )

About the Journal

  • 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.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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