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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 12

Heterogeneity aware adaptive federated security aggregation method

Li Xiaohuia
Wang Chaoa
Wang Shiyuana
Zhang Xingb
Lan Jiec
a. School of Electronics & Information Engineering, b. Smart Campus Construction Center, c. College of Science, Liaoning University of Technology, Jinzhou Liaoning 121001, China
DOI: 10.19734/j.issn.1001-3695.2025.04.0142

Abstract

This paper proposes the Heterogeneity Aware Adaptive Federated Security Aggregation Method (HAFSA) to address the challenges of model performance degradation in non-independent and identically distributed (Non-IID) data environments, insufficient dynamic sensitivity privacy protection, and robustness imbalance caused by malicious attacks and communication delays in federated learning. The method contained three key components. First, the server dynamically adjusted aggregation weights based on local model deviation degrees and performed latency compensation for delayed clients. Second, clients added local noise to sensitive layers according to gradient significance while the server dynamically allocated central privacy budgets by measuring privacy levels through local update deviations. Third, the server selected valid clients using interquartile range method by combining loss values and model update similarities, then implemented truncated median aggregation for these selected clients. Experimental results showed that HAFSA outperformed comparative algorithms on the CIFAR-10 and MNIST datasets, with accuracy improved by 12%-20%, convergence speed more than doubled, and effective resistance to gradient leakage attacks. Ablation experiments further verified the significant contribution of each component to system performance. The experiments confirmed that HAFSA has achieved an efficient balance among privacy protection, communication efficiency, and model robustness in asynchronous federated learning.

Foundation Support

国家自然科学基金资助项目(62203201、61802161)
辽宁省应用基础研究计划项目(2022JH2/101300278)
2024年辽宁省属本科高校基本科研业务费专项资金资助项目(LJZZ212410154025)
辽宁工业大学项目(xjg2022092)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.04.0142
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 12

Publish History

[2025-08-05] Accepted Paper

Cite This Article

李晓会, 王超, 王诗源, 等. 异质性感知的自适应安全联邦聚合方法 [J]. 计算机应用研究, 2025, 42 (12). (2025-08-06). https://doi.org/10.19734/j.issn.1001-3695.2025.04.0142. (Li Xiaohui, Wang Chao, Wang Shiyuan, et al. Heterogeneity aware adaptive federated security aggregation method [J]. Application Research of Computers, 2025, 42 (12). (2025-08-06). https://doi.org/10.19734/j.issn.1001-3695.2025.04.0142. )

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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