Federated Learning

Federated Learning enables distributed model training while preserving data privacy by keeping data localized. Instead of centralizing data, the model is sent to where the data resides, trained locally, and only model updates are shared back. These updates are then aggregated to improve the global model. The process includes challenges like dealing with non-IID data, communication efficiency, and model convergence.

Healthcare institutions collaboratively training diagnosis models while keeping patient data private, Mobile keyboards improving text prediction without sharing user data, IoT devices learning from local sensor data while maintaining privacy