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<p>Federated Learning (FL) is a privacy-enhancing technique that enables multiple participants to collaboratively train machine learning models without sharing their local data. While FL is a promising paradigm, it is vulnerable to attacks targeting model updates and malicious behavior from clients. To address these challenges, we propose a Privacy-Preserving and High-Secure FL (PPHSFL) scheme, incorporating Models Randomization and Compensation (MRC) and Adaptive Defensive Rewards (ADR). MRC involves using a randomized discrete loss function in FL training to prevent gradient backward inference, enhancing model security. ADR counters dishonest client attacks through adaptive client selection and dynamic rewards. Our suggested technique ensures customer privacy preservation and mitigates threats during FL processing. Numerical analysis and performance evaluation demonstrate the efficacy of our approach compared to existing methods. The PPHSFL method has an average accuracy improvement of 3.0% for the cutting-edge method.</p>

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Based on model randomization and adaptive defense for federated learning schemes

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月24日

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  • Gaofeng Yue,
  • Xiaowei Han

摘要

Federated Learning (FL) is a privacy-enhancing technique that enables multiple participants to collaboratively train machine learning models without sharing their local data. While FL is a promising paradigm, it is vulnerable to attacks targeting model updates and malicious behavior from clients. To address these challenges, we propose a Privacy-Preserving and High-Secure FL (PPHSFL) scheme, incorporating Models Randomization and Compensation (MRC) and Adaptive Defensive Rewards (ADR). MRC involves using a randomized discrete loss function in FL training to prevent gradient backward inference, enhancing model security. ADR counters dishonest client attacks through adaptive client selection and dynamic rewards. Our suggested technique ensures customer privacy preservation and mitigates threats during FL processing. Numerical analysis and performance evaluation demonstrate the efficacy of our approach compared to existing methods. The PPHSFL method has an average accuracy improvement of 3.0% for the cutting-edge method.