Reliable and Privacy-Preserving Federated Learning with Anomalous Users

作     者：ZHANG Weiting LIANG Haotian XU Yuhua ZHANG Chuan ZHANG Weiting;LIANG Haotian;XU Yuhua;ZHANG Chuan

作者机构：Beijing Jiaotong UniversityBeijing 100091China Beijing Institute of TechnologyBeijing 100081China 

出 版 物：《ZTE Communications》 (中兴通讯技术（英文版）)

年 卷 期：2023年第21卷第1期

页      面：15-24页

学科分类：08[工学] 081104[工学-模式识别与智能系统] 0811[工学-控制科学与工程] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported in part by the Fundamental Research Funds for Central Universities under Grant No.2022RC006 in part by the National Nat⁃ural Science Foundation of China under Grant Nos.62201029 and 62202051 in part by the BIT Research and Innovation Promoting Project under Grant No.2022YCXZ031 in part by the Shandong Provincial Key Research and Development Program under Grant No.2021CXGC010106 in part by the China Postdoctoral Science Foundation under Grant Nos.2021M700435,2021TQ0042,2021TQ0041,BX20220029 and 2022M710007. 

主　　题：federated learning anomalous user privacy preservation reliability homomorphic cryptosystem 

摘      要：Recently,various privacy-preserving schemes have been proposed to resolve privacy issues in federated learning(FL).However,most of them ignore the fact that anomalous users holding low-quality data may reduce the accuracy of trained models.Although some existing works manage to solve this problem,they either lack privacy protection for users’sensitive information or introduce a two-cloud model that is difficult to find in reality.A reliable and privacy-preserving FL scheme named reliable and privacy-preserving federated learning(RPPFL)based on a single-cloud model is proposed.Specifically,inspired by the truth discovery technique,we design an approach to identify the user’s reliability and thereby decrease the impact of anomalous users.In addition,an additively homomorphic cryptosystem is utilized to provide comprehensive privacy preservation(user’s local gradient privacy and reliability privacy).We give rigorous theoretical analysis to show the security of RPPFL.Based on open datasets,we conduct extensive experiments to demonstrate that RPPEL compares favorably with existing works in terms of efficiency and accuracy.