AI can enhance health care by using data to create useful models. However, sharing data between institutions is challenging due to legal and privacy issues. Federated learning (FL) allows institutions to train AI models without sharing data, but it also has its own security concerns. As FL becomes more commonplace in health care, it is crucial to understand its risks. This work reviews the literature on privacy-preserving FL, highlighting threats and solutions, aiming to guide health-care researchers on FL’s security and privacy aspects.
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【저자키워드】 Health care, federated learning, privacy, security, review article,
【저자키워드】 Health care, federated learning, privacy, security, review article,