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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 3

Federated learning scheme based on adaptive noise and dynamic weighting

Wang Honglin1a
Xue Shan1b
Zhu Cheng2
1. a. School of Artificial Intelligence (College of Future Technology), b. School of Computer science, Nanjing University of Information Science & Technology, Nanjing Jiangsu 210044, China
2. Electrical & Computer Engineering, University of Illinois at Urbana Champaign, Urbana IL 61801, USA
DOI: 10.19734/j.issn.1001-3695.2024.08.0299

Abstract

Applying differential privacy to federated learning is one of the effective methods to protect the privacy of training data, but adding fixed noise to model training in existing algorithms will lead to the problem of low model accuracy and data privacy leakage. Therefore, this paper proposed a federation learning algorithm based on adaptive noise and dynamic weighting. Firstly, considering the heterogeneity of the gradient, the algorithm predicts the current round gradient norm for each client, obtains the clipping threshold, and performs adaptive clipping gradients for different rounds to achieve adaptive noise adjustment. Secondly, in order to further improve the efficiency of model training, the algorithm also proposed a dynamic weighted model aggregation method combining client contribution and data volume. The experimental results show that our algorithm not only improves the accuracy by 5.03%, 2.94% and 2.85%, but also improves the training rounds by about 5-40 rounds compared with DP-FL and the other two adaptive noise algorithms under the premise of differential privacy.

Foundation Support

国家自然科学基金委员会青年项目(面向高动态无线通信信道特征分析与建模理论研究(62101275)
大规模机器类通信信道估计与用户检测技术研究(62101274)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.08.0299
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 3

Publish History

[2024-12-11] Accepted Paper

Cite This Article

王红林, 薛珊, 朱丞. 基于自适应噪声和动态加权的联邦学习算法 [J]. 计算机应用研究, 2025, 42 (3). (2024-12-16). https://doi.org/10.19734/j.issn.1001-3695.2024.08.0299. (Wang Honglin, Xue Shan, Zhu Cheng. Federated learning scheme based on adaptive noise and dynamic weighting [J]. Application Research of Computers, 2025, 42 (3). (2024-12-16). https://doi.org/10.19734/j.issn.1001-3695.2024.08.0299. )

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.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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