Federated Learning

Privacy-Preserving ML

Train on distributed data.

Overview

Data stays local. Only model updates shared. Central server aggregates updates.

Challenges

Communication efficiency. Non-IID data across clients. Privacy guarantees.

Implementations

TensorFlow Federated, PySyft. Differential privacy adds more privacy.

Applications

Mobile keyboards. Healthcare. Cross-organization collaboration.

Key Takeaways

1. Train without sharing raw data
2. Only model updates transmitted
3. Handles non-IID data challenges