Privacy-Preserving Computation: Federated Learning
Privacy-Preserving Computation: Federated Learning Federated learning lets devices learn together without sending raw data to a central server. Each device trains a local model on its own data and shares only small updates. The server combines those updates to build a global model. This keeps personal data on the device, reducing exposure and meeting privacy goals. In practice, the process starts with a global model. In rounds, a subset of devices downloads the model, trains for a bit on their data, and sends back updates. The central server averages these updates to form a new global model. This setup works well for mobile apps, smart devices, and services that touch many users. It can be enhanced with privacy tools to further protect individual data. ...