FedCVG: a two-stage robust federated learning optimization algorithm.

Journal: Scientific reports
Published Date: May 26, 2025

Abstract

Federated learning provides an effective solution to the data privacy issue in distributed machine learning. However, distributed federated learning systems are inherently susceptible to data poisoning attacks and data heterogeneity. Under conditions of high data heterogeneity, the gradient conflict problem in federated learning becomes more pronounced, making traditional defense mechanisms against poisoning attacks less adaptable between scenarios with and without attacks. To address this challenge, we design a two-stage federated learning framework for defending against poisoning attacks-FedCVG. During implementation, FedCVG first removes malicious clients using a reputation-based clustering method, and then optimizes communication overhead through a virtual aggregation mechanism. Extensive experimental results show that, compared to other baseline methods, FedCVG improves average accuracy by 4.2% and reduces communication overhead by approximately 50% while defending against poisoning attacks.

Authors

  • Runze Zhang
    Shiley Eye Institute, Institute for Engineering in Medicine, Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA 92093, USA.
  • Yang Zhang
    Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
  • Yating Zhao
    School of Management, Hefei University of Technology, Hefei 230009, China.
  • Bin Jia
    Department of Anesthesiology, Xuanwu Hospital, Capital Medical University Beijing, China.
  • Wenjuan Lian
    College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China.

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