What is federated learning in MLOps?

Federated learning trains models across decentralized devices without sharing raw data.

Each client trains locally and shares gradients or model updates with a central server. This preserves privacy and reduces data transfer. Challenges include communication cost, heterogeneity, and aggregation stability.