Securing the collective intelligence: a comprehensive review of federated learning security attacks and defensive strategies

作     者：Kaushal, Vishal Sharma, Sangeeta 

作者机构：Natl Inst Technol Comp Sci & Engn Dept Hamirpur 177005 Himachal Prades India 

出 版 物：《KNOWLEDGE AND INFORMATION SYSTEMS》 (Knowl. Inf. Systems. Syst.)

年 卷 期：2025年第67卷第4期

页      面：3099-3137页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 08[工学] 070105[理学-运筹学与控制论] 081101[工学-控制理论与控制工程] 0701[理学-数学] 071101[理学-系统理论] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Not funded 

主　　题：Centralized learning Federated learning Threats Defense Aggregation algorithm 

摘      要：Federated learning holds significant potential as a collaborative machine learning technique, allowing multiple entities to work together on a collective model without the need to exchange data. However, due to the distribution of data across multiple devices, federated learning becomes susceptible to a range of attacks. This paper provides an extensive examination of the different forms of attacks that can target federated learning systems. The attacks discussed include data poisoning attacks, model poisoning attacks, backdoor attacks, Byzantine attacks, membership inference attacks, model inversion attacks, etc. Each attack is examined in detail, with examples from the literature provided. Additionally, potential countermeasures to defend against these attacks are explored. The objective of this review is to provide an in-depth survey of the current landscape in federated learning attacks and corresponding defense mechanisms.