ModelRefs / Federated Learning — AI Glossary
Federated Learning — AI Glossary
A distributed training paradigm where each client trains on local data and only sends model updates to a central server, preserving data privacy.
Overview
Federated learning (McMahan et al. 2017, Google) enables training on sensitive data (medical records, financial transactions) without centralizing data. FedAvg aggregates client model gradients. Challenges: non-IID client data distributions, communication overhead, and Byzantine-robust aggregation. Used in Gboard keyboard next-word prediction and clinical NLP.
Reference details
| Topic | safety |
|---|---|
| Last reviewed | 2026-06-24 |
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Frequently asked questions
What is Federated Learning?
A distributed training paradigm where each client trains on local data and only sends model updates to a central server, preserving data privacy.
What concepts are related to Federated Learning?
Closely related concepts include differential privacy, data privacy, model risk management.