DP2Guard: A Lightweight and Byzantine-Robust Privacy-Preserving Federated Learning Scheme for Industrial IoT

• URL: http://arxiv.org/abs/2507.16134v1
• Date: Tue, 22 Jul 2025 01:06:39 GMT
• Title: DP2Guard: A Lightweight and Byzantine-Robust Privacy-Preserving Federated Learning Scheme for Industrial IoT
• Authors: Baofu Han, Bing Li, Yining Qi, Raja Jurdak, Kaibin Huang, Chau Yuen,
• Abstract summary: DP2Guard is a lightweight PPFL framework that enhances both privacy and robustness.<n>A hybrid defense strategy is proposed, which extracts gradient features using singular value decomposition and cosine similarity.<n>A trust score-based adaptive aggregation scheme adjusts client weights according to historical behavior.
• Score: 37.44256772381154
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Privacy-Preserving Federated Learning (PPFL) has emerged as a secure distributed Machine Learning (ML) paradigm that aggregates locally trained gradients without exposing raw data. To defend against model poisoning threats, several robustness-enhanced PPFL schemes have been proposed by integrating anomaly detection. Nevertheless, they still face two major challenges: (1) the reliance on heavyweight encryption techniques results in substantial communication and computation overhead; and (2) single-strategy defense mechanisms often fail to provide sufficient robustness against adaptive adversaries. To overcome these challenges, we propose DP2Guard, a lightweight PPFL framework that enhances both privacy and robustness. DP2Guard leverages a lightweight gradient masking mechanism to replace costly cryptographic operations while ensuring the privacy of local gradients. A hybrid defense strategy is proposed, which extracts gradient features using singular value decomposition and cosine similarity, and applies a clustering algorithm to effectively identify malicious gradients. Additionally, DP2Guard adopts a trust score-based adaptive aggregation scheme that adjusts client weights according to historical behavior, while blockchain records aggregated results and trust scores to ensure tamper-proof and auditable training. Extensive experiments conducted on two public datasets demonstrate that DP2Guard effectively defends against four advanced poisoning attacks while ensuring privacy with reduced communication and computation costs.

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