Biomedical research is increasingly shaped by vast and diverse datasets, yet their integration is constrained by privacy concerns, regulatory barriers, and fragmented infrastructures. Federated learning (FL) has emerged as a promising paradigm that enables institutions to collaboratively train machine learning models while keeping sensitive data local. This approach has the potential to accelerate discovery in areas such as precision medicine, rare disease research, and population health by pooling knowledge without centralizing data. However, federated learning also introduces new ethical and governance challenges. Risks of information leakage, inequitable participation, algorithmic bias, u... More >