Trustworthy artificial intelligence (AI) technology has revolutionized daily life and greatly benefited human society. Among various AI technologies, Federated Learning (FL) stands out as a promising solution for diverse real-world scenarios, ranging from risk evaluation systems in finance to cutting-edge technologies like drug discovery in life sciences. However, challenges around data isolation and privacy threaten the trustworthiness of FL systems. Adversarial attacks against data privacy, learning algorithm stability, and system confidentiality are particularly concerning in the context of distributed training in federated learning. Therefore, it is crucial to develop FL in a trustworthy manner, with a focus on security, robustness, and privacy. In this survey, we propose a comprehensive roadmap for developing trustworthy FL systems and summarize existing efforts from three key aspects: security, robustness, and privacy. We outline the threats that pose vulnerabilities to trustworthy federated learning across different stages of development, including data processing, model training, and deployment. To guide the selection of the most appropriate defense methods, we discuss specific technical solutions for realizing each aspect of Trustworthy FL (TFL). Our approach differs from previous work that primarily discusses TFL from a legal perspective or presents FL from a high-level, non-technical viewpoint.

翻译：可信人工智能技术已深刻变革日常生活，并为人类社会带来巨大福祉。在众多人工智能技术中，联邦学习作为一种极具前景的解决方案涌现，其应用场景涵盖金融领域风险评估系统至生命科学中药物发现等尖端技术。然而，数据孤岛与隐私挑战正威胁着联邦学习系统的可信性。针对数据隐私、学习算法稳定性及系统保密性的对抗攻击，在联邦学习分布式训练场景中尤为令人担忧。因此，以安全性、鲁棒性和隐私为核心，以可信方式发展联邦学习至关重要。本综述提出开发可信联邦学习系统的系统性路线图，并从安全性、鲁棒性和隐私三大关键维度总结现有研究成果。我们梳理了联邦学习开发各阶段（包括数据处理、模型训练与部署）中危及可信性的威胁因素。为指导最优防御方法的选择，本文详述了实现可信联邦学习各维度的具体技术方案。与既往主要从法律视角探讨可信联邦学习或仅以非技术性高层视角介绍联邦学习的研究不同，本文采取了截然不同的研究路径。