  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.
