What is Federated Learning?
Federated Learning Definition: Training AI models without gathering raw data centrally—devices or institutions keep their data locally, sending only model updates.
Understanding Federated Learning
Federated Learning fundamentally changes machine learning development by training models across multiple data sources without aggregating raw data in one location. Instead of uploading sensitive records to a central server, each participating device or institution locally trains on its own data and sends only model updates to a central aggregator, which combines them into a global model. This approach mitigates privacy and regulatory barriers—healthcare organizations can collaborate without exposing patient records, or mobile apps can learn from user data without uploading it. However, federated learning introduces unique challenges: ensuring updates are not malicious or leaking info (poisoning attacks), addressing non-IID distributions (data differs across participants), handling variable network connectivity or resource constraints, and integrating privacy-enhancing techniques like differential privacy or secure aggregation. Organizations adopt federated learning for scenarios like cross-hospital medical AI, on-device personalization, or multi-enterprise data partnerships. Implementations must carefully coordinate participants, define communication protocols, manage trust boundaries, and design fallback solutions if some participants are compromised or drop out. Though still maturing, federated learning promises a more secure, privacy-friendly way to harness large-scale data from disparate entities.