Federated Learning

The core insight of federated learning is elegant: instead of bringing data to the model, bring the model to the data. Each participating node (a hospital, a bank branch, an edge device) trains the shared model on its local data and sends only the resulting gradient updates or weight deltas back to a central aggregator. The aggregator merges these updates into an improved global model, which is then redistributed — and no raw data ever crosses organizational or jurisdictional boundaries.

The enterprise value proposition is clearest in regulated industries. Healthcare consortia can collaboratively train diagnostic AI models across dozens of hospital networks without sharing patient records, satisfying HIPAA and GDPR simultaneously. Financial institutions can collaborate on fraud detection models without exposing transaction data to competitors. Telecom operators can improve on-device keyboards and voice recognition without uploading personal communications to the cloud.

Three architectural variants matter in practice: **horizontal federated learning** (participants share the same feature space but different records — most common), **vertical federated learning** (participants hold different features about the same individuals — relevant for bank-telecom collaborations), and **federated transfer learning** (participants have both different feature spaces and different samples — the hardest problem). Enterprise deployments must also address the "honest-but-curious" aggregator problem: even gradient updates can leak information about training data, which is why federated learning is typically paired with differential privacy noise injection and secure aggregation protocols.