# Federated Learning
Confidence: high
Last verified: 2026-05-22
Generation: human_only


## TL;DR

Federated Learning (FL) trains ML models across decentralized devices holding local data, without centralizing the data. Devices train locally, share only model updates (not raw data) with a central server, which aggregates updates. Used by Google (Gboard keyboard) and Apple for privacy-preserving ML.

## Core Explanation

Federated Averaging (FedAvg): server averages model weights from devices. Challenges: non-IID data (different users have different patterns), communication efficiency (devices on mobile networks), system heterogeneity (different hardware capabilities), privacy guarantees (differential privacy + secure aggregation). FL is a key enabler for privacy-respecting AI on sensitive data (healthcare, finance).

## Further Reading

- [Communication-Efficient Learning of Deep Networks from Decentralized Data (McMahan et al., 2017)](https://arxiv.org/abs/1602.05629)