federated learning

Federated learning trains models on decentralized data without centralizing raw data, preserving privacy. **Mechanism**: Central server sends model to devices/clients, each client trains on local data, clients send model updates (not data) to server, server aggregates updates (FedAvg: average weights), repeat until convergence. **Privacy benefits**: Raw data never leaves device, only model updates transmitted, can combine with differential privacy on updates. **Applications**: Mobile keyboards (next word prediction), healthcare (cross-hospital learning), finance (fraud detection across banks), IoT devices. **Challenges**: **Non-IID data**: Client data differently distributed, hurts convergence. **Communication**: Model updates expensive to transmit frequently. **Device heterogeneity**: Different compute capabilities. **Stragglers**: Slow clients delay rounds. **Adversarial clients**: May send malicious updates. **Aggregation methods**: FedAvg (weighted average), FedProx (regularization), personalized variants. **Privacy considerations**: Updates can still leak information - use secure aggregation, differential privacy. **Frameworks**: TensorFlow Federated, PySyft, Flower. **Trade-offs**: Privacy vs accuracy vs communication cost. Enables ML where data sharing is impossible.