Overview
Federated learning allows for 'privacy-preserving' AI. Instead of sending all data to a central server, the model is sent to the data.
The Process
- A central server sends a 'base model' to many devices (e.g., smartphones).
- Each device trains the model on its local data.
- The devices send only the 'updates' (gradients) back to the server.
- The server aggregates the updates to improve the global model and sends it back out.
Applications
- Improving predictive text on mobile keyboards (e.g., Gboard).
- Medical research across different hospitals without sharing patient records.
- IoT device optimization.