Federated Learning

Federated learning allows many participants to improve a shared model while keeping raw training data on local devices or institutional servers. A coordinator distributes a model, participants calculate updates from their own data, and aggregated updates are used to create the next version. This can reduce data movement and support privacy or regulatory requirements in mobile, healthcare, and cross-organization settings. It does not guarantee privacy by itself: model updates may leak information, participants can submit malicious changes, and uneven local datasets can destabilize training. Secure aggregation, differential privacy, authentication, robust update rules, and careful governance are often needed alongside the core federated process.