While recent years have witnessed the advancement in big data and Artificial Intelligence (AI), it is of much importance to safeguard data privacy and security. As an innovative approach, Federated Learning (FL) addresses these concerns by facilitating collaborative model training across distributed data sources without transferring raw data. However, the challenges of robust security and privacy across decentralized networks catch significant attention in dealing with the distributed data in FL. In this paper, we conduct an extensive survey of the security and privacy issues prevalent in FL, underscoring the vulnerability of communication links and the potential for cyber threats. We delve into various defensive strategies to mitigate these risks, explore the applications of FL across different sectors, and propose research directions. We identify the intricate security challenges that arise within the FL frameworks, aiming to contribute to the development of secure and efficient FL systems.

翻译：尽管近年来大数据与人工智能（AI）取得了显著进展，保障数据隐私与安全的重要性日益凸显。联邦学习（FL）作为一种创新方法，通过促进分布式数据源间的协同模型训练而无需传输原始数据，有效应对了上述关切。然而，去中心化网络中鲁棒的安全与隐私挑战在处理FL分布式数据时备受关注。本文对FL中普遍存在的安全与隐私问题进行了全面综述，重点分析了通信链路的脆弱性及潜在网络威胁。我们深入探讨了多种缓解这些风险的防御策略，考察了FL在不同领域的应用，并提出了未来研究方向。通过识别FL框架中复杂的安全挑战，本文旨在为构建安全高效的FL系统贡献力量。