  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.
