Federated Learning

Retired

No longer on the radar

We removed this entry in the September 2026 release. Retiring is not a recommendation against it — it means the radar no longer needs a separate opinion here. The write-up below is kept as it was last published and is no longer maintained.

This decision is now covered by:

Overview

Federated learning trains models across decentralized data without raw centralization, using frameworks like Flower (Flower).

Assess for cross-institution or on-device scenarios with strict data residency. Expect engineering overhead for aggregation, drift, and secure aggregation protocols.

Sources