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.

本研究针对联邦学习中存在的安全和隐私问题，进行深入调查，指出了通信链路的脆弱性及潜在的网络攻击威胁。通过探讨多种防御策略及应用场景，本文为建立安全高效的联邦学习系统提供了新思路和研究方向。

可信的联邦学习：隐私、安全及其他