Recent advances in communication technologies and Internet-of-Medical-Things
have transformed smart healthcare enabled by artificial intelligence (AI).
Traditionally, AI techniques require centralized data collection and processing
that may be infeasible in realistic healthcare scenarios due to the high
scalability of modern healthcare networks and growing data privacy concerns.
Federated Learning (FL), as an emerging distributed collaborative AI paradigm,
is particularly attractive for smart healthcare, by coordinating multiple
clients (e.g., hospitals) to perform AI training without sharing raw data.
Accordingly, we provide a comprehensive survey on the use of FL in smart
healthcare. First, we present the recent advances in FL, the motivations, and
the requirements of using FL in smart healthcare. The recent FL designs for
smart healthcare are then discussed, ranging from resource-aware FL, secure and
privacy-aware FL to incentive FL and personalized FL. Subsequently, we provide
a state-of-the-art review on the emerging applications of FL in key healthcare
domains, including health data management, remote health monitoring, medical
imaging, and COVID-19 detection. Several recent FL-based smart healthcare
projects are analyzed, and the key lessons learned from the survey are also
highlighted. Finally, we discuss interesting research challenges and possible
directions for future FL research in smart healthcare.

本综述介绍 FL 在智能医疗中的应用，包括资源感知型 FL，安全和隐私意识型 FL，激励型 FL 和个性化 FL，重点关注 FL 在健康数据管理，远程健康监测，医学成像和 COVID-19 检测方面的新领域，分析了几个最近基于 FL 的智能医疗项目，并讨论了未来研究方向和挑战。