What is Federated Learning?

Training AI Models While Protecting Data Privacy

Decentralized Machine Learning for Sensitive Data

Enabling Collaborative AI Without Centralized Data

**Federated Learning** is a decentralized machine learning approach that allows AI models to be **trained on local datasets across multiple devices or servers without centralizing the raw data**. Instead of sending private user data to a central server for training, only the model updates (e.g., learned weights) are sent back and aggregated. This significantly enhances **data privacy and security**, as sensitive information never leaves the user's device. Federated learning is crucial for developing privacy-preserving AI applications in sectors like healthcare, finance, and mobile computing, becoming an increasingly important aspect of **Responsible AI** development in 2025. 🔒📱