Federated learning allows AI models to train on decentralized data sources. Each device contributes to the model by sending only updates, not raw data. This method protects privacy and enhances security while still enabling AI to improve. Common applications include predictive keyboards, health tracking, and recommendation systems. Federated learning ensures personalized AI performance while maintaining user confidentiality, balancing innovation and safety.
**Key takeaway:** AI can learn without sharing your private data.