Data privacy has become a major concern in healthcare due to the increasing
digitization of medical records and data-driven medical research. Protecting
sensitive patient information from breaches and unauthorized access is
critical, as such incidents can have severe legal and ethical complications.
Federated Learning (FL) addresses this concern by enabling multiple healthcare
institutions to collaboratively learn from decentralized data without sharing
it. FL's scope in healthcare covers areas such as disease prediction, treatment
customization, and clinical trial research. However, implementing FL poses
challenges, including model convergence in non-IID (independent and identically
distributed) data environments, communication overhead, and managing
multi-institutional collaborations. A systematic review of FL in healthcare is
necessary to evaluate how effectively FL can provide privacy while maintaining
the integrity and usability of medical data analysis. In this study, we analyze
existing literature on FL applications in healthcare. We explore the current
state of model security practices, identify prevalent challenges, and discuss
practical applications and their implications. Additionally, the review
highlights promising future research directions to refine FL implementations,
enhance data security protocols, and expand FL's use to broader healthcare
applications, which will benefit future researchers and practitioners.

通过分析医疗保健领域中已经存在的研究文献，本研究系统评估了联邦学习在提供隐私保护的同时维护医疗数据分析的完整性和可用性方面的效力，同时探讨了实际应用及其影响，突出未来的研究方向，旨在完善联邦学习的实现、增强数据安全协议，并将联邦学习拓展到更广泛的医疗应用领域，为未来的研究人员和实践者提供帮助。