Daniel Beutel - Keynote: Federated Learning with Flower: AI's Next Frontier | PyData Global 2023

Federated Learning enables training AI models across distributed data sources without centralizing sensitive data, addressing privacy and regulatory concerns

Key use cases include:

• Healthcare (patient data across hospitals)
• Finance (fraud detection across institutions)
• Personal computing (mobile devices)
• Manufacturing (predictive maintenance)
• Automotive (self-driving data)

Flower framework provides:

• Infrastructure for federated learning implementation
• Support for multiple ML frameworks (PyTorch, TensorFlow etc.)
• Simulation capabilities for testing
• Cross-device and cross-silo federation options
• Privacy-preserving features

The federated learning process:

1. Server initializes model
2. Model sent to distributed client devices
3. Clients train locally on their data
4. Updated models sent back to server
5. Server aggregates updates into global model
6. Process repeats in rounds

Key challenges to consider:

• Data format standardization across silos
• Security and privacy requirements
• Communication efficiency
• Model aggregation strategies
• Client selection and coordination

Many valuable datasets remain inaccessible for AI training due to:

• Privacy concerns
• Regulatory restrictions
• Geographic distribution
• Organizational boundaries

Open source community adoption:

• 100+ contributors
• 1000+ dependent projects
• Global user base across industries
• Active development and support