PRFL: Achieving Efficient Robust Aggregation in Privacy-Preserving Federated Learning

作者：Xun Liu, Jian Yang, Shuangwu Chen, Huasen He, Xiaofeng Jiang · 发表于：IEEE Transactions on Network and Service Management · 年份：2024 · DOI：10.1109/tnsm.2024.3514212 · 被引用次数：5 · 研究领域：Privacy-Preserving Technologies in Data

Robust Privacy-Preserving Federated Learning (PPFL) is a secure distributed machine learning paradigm designed for untrusted environments, aiming to aggregate gradients while ensuring the reliability of the results without disclosing user gradients. However, existing single-server robust PPFL schemes require users to generate commitments in each aggregation round to ensure the correctness of the aggregation results, which leads to high computational overhead. We propose an efficient single-server robust PPFL scheme named Privacy-Preserving Robust Federated Learning (PRFL). PRFL achieves efficient gradient aggregation through a “detection-identification-exclusion” strategy. PRFL only performs quick detection without requiring high-complexity commitments in most of aggregation round, thereby ensuring excellent efficiency. PRFL comprises three pivotal components: Privacy-Preserving Gradient Aggregation Based on Packed Secret Sharing (PGAPS), Swift Share Verification based on Dual Codes (SSVDC), and Commitment-based Malicious User Identification (CMUI). PGAPS is utilized to implement the FLTrust rule without disclosing gradients. SSVDC swiftly detects incorrect shares without using commitments. CMUI identifies malicious users when SSVDC detects incorrect shares. Experimental results demonstrate the robustness and efficiency of PRFL. In a PPFL system with 100 users, PRFL can robustly aggregate gradients of a million dimensions within 37 seconds of average computational time.