Federated learning trains AI models across decentralized devices without centralizing sensitive data. This privacy-preserving approach enables collaborative machine learning while protecting user information and reducing bandwidth requirements.

How It Works

• Local Training: Models train on user devices using local data privately.

• Model Aggregation: Central server combines updates without accessing raw data.

• Distributed Updates: Improved global model redistributed to devices.

• Privacy Preservation: Individual data never leaves user devices.

• Differential Privacy: Additional techniques prevent information leakage.

Applications

Mobile keyboard predictions improve across millions of devices, healthcare research analyzes patient data while maintaining privacy, financial fraud detection learns from distributed transaction patterns, autonomous vehicles share driving knowledge securely, and IoT devices collaborate for smarter operations.

Benefits

Enhanced privacy keeps sensitive data on user devices, reduced bandwidth avoids transferring large datasets, regulatory compliance meets GDPR and privacy requirements, edge intelligence leverages device computational power, and improved models benefit from diverse data sources.

Challenges

Communication overhead requires efficient update protocols, heterogeneous devices have varying computational capabilities, non-IID data across devices affects model quality, security threats include model poisoning attacks, and debugging distributed systems is complex.