The paper "Federated Learning for Healthcare Informatics" by Jie Xu, Benjamin S. Glicksberg, Chang Su, Peter Walker, Jiang Bian, and Fei Wang reviews the application of federated learning in healthcare. Federated learning is a paradigm that enables the training of a shared global model while keeping sensitive data at the local institutions, addressing the challenge of fragmented and private healthcare data. The authors summarize the statistical, system, and privacy challenges in federated learning and discuss solutions such as consensus and pluralistic approaches. They highlight the importance of communication efficiency and privacy-preserving schemes, including secure multi-party computation and differential privacy. The paper also reviews applications of federated learning in healthcare, such as patient similarity learning, phenotyping, and predictive modeling using electronic health records (EHRs). It discusses the potential of federated learning to improve the quality of care delivery by leveraging diverse and representative EHR data. The authors conclude by outlining open questions and future directions, including data quality, incorporating expert knowledge, incentive mechanisms, personalization, and model precision.The paper "Federated Learning for Healthcare Informatics" by Jie Xu, Benjamin S. Glicksberg, Chang Su, Peter Walker, Jiang Bian, and Fei Wang reviews the application of federated learning in healthcare. Federated learning is a paradigm that enables the training of a shared global model while keeping sensitive data at the local institutions, addressing the challenge of fragmented and private healthcare data. The authors summarize the statistical, system, and privacy challenges in federated learning and discuss solutions such as consensus and pluralistic approaches. They highlight the importance of communication efficiency and privacy-preserving schemes, including secure multi-party computation and differential privacy. The paper also reviews applications of federated learning in healthcare, such as patient similarity learning, phenotyping, and predictive modeling using electronic health records (EHRs). It discusses the potential of federated learning to improve the quality of care delivery by leveraging diverse and representative EHR data. The authors conclude by outlining open questions and future directions, including data quality, incorporating expert knowledge, incentive mechanisms, personalization, and model precision.