federated learning Articles

Federated learning is a privacy-preserving technique for training machine learning models on decentralized data sources, such as electronic health records from multiple healthcare providers. It enables collaborative model development while keeping sensitive patient data securely in place, addressing data privacy and regulatory concerns. Federated learning holds promise for developing more robust AI models in healthcare by leveraging diverse, distributed data sources without compromising patient confidentiality.