Computer Engineering and Applications ›› 2024, Vol. 60 ›› Issue (3): 61-77.DOI: 10.3778/j.issn.1002-8331.2303-0332

• Research Hotspots and Reviews • Previous Articles     Next Articles

Review of Research on Information Security in Federated Learning

DUAN Xinru, CHEN Guirong, CHEN Aiwang, CHEN Chen, JI Weifeng   

  1. College of Information and Navigation, Air Force Engineering University, Xi’an 710077, China
  • Online:2024-02-01 Published:2024-02-01



  1. 空军工程大学 信息与导航学院,西安 710077

Abstract: As a new machine learning technology, federated learning allows participants to complete collaborative training and obtain global models through parameter interaction without sharing original data. It provides a new paradigm for breaking data silos and integrating data resources and has become a research hotspot in the field of artificial intelligence. However, federated learning still faces many security risks. This paper systematically analyzes and classifies the latest research results in the field of federated learning at home and abroad. Taking the training process of the federated learning model as a clue, it analyzes the security threats that may exist in the system during each process, studies the mechanism and characteristics of different security threats, and classifies them according to the degree of threat. Based on this, the paper studies the current advanced defense strategies. Finally, it discusses the main challenges and future development directions of federated learning in order to promote the safe landing and promotion of federated learning applications.

Key words: federated learning, data security, system threat, defense strategy

摘要: 联邦学习作为一种新兴的机器学习技术,允许多方在不共享原始数据的前提下通过参数交互完成协同训练,生成全局模型,为打破数据孤岛、整合数据资源提供了新范式,成为人工智能领域的一大研究热点。但联邦学习依然面临诸多安全风险。对联邦学习领域的国内外最新研究成果进行系统分析和分类,以联邦学习模型训练过程为线索,分析每个过程中系统可能存在的安全威胁,研究不同安全威胁的机理和特点,并按照威胁程度对其进行分类,在此基础上,研究当前先进的防御策略;最后,探讨了联邦学习面临的主要挑战和未来发展方向,旨在推动联邦学习应用安全落地和推广。

关键词: 联邦学习, 数据安全, 系统威胁, 防御策略