#federated-learning

Implement federated learning systems for privacy-preserving machine learning across distributed data sources. Federated learning architecture: 1. Central server: model aggregation, global model updates, coordination protocol. 2. Client devices: local training, gradient computation, privacy preservation techniques. 3. Communication protocol: secure aggregation, differential privacy, encrypted gradients. Training process: 1. Model distribution: send global model to participating clients, version synchronization. 2. Local training: client-specific data, personalized updates, local epochs (5-10). 3. Aggregation: FedAvg (weighted averaging), secure aggregation, Byzantine fault tolerance. Privacy techniques: 1. Differential privacy: noise addition, privacy budget (ε=1-10), privacy accounting. 2. Secure multi-party computation: gradient sharing without data exposure, cryptographic protocols. 3. Homomorphic encryption: computation on encrypted data, privacy-preserving aggregation. Data heterogeneity: 1. Non-IID data: statistical heterogeneity, system heterogeneity, client drift. 2. Personalization: per-client adaptation, meta-learning approaches, personalized layers. 3. Clustering: client clustering, similar data distribution grouping, hierarchical federated learning. System challenges: 1. Communication efficiency: gradient compression, sparse updates, periodic aggregation. 2. Fault tolerance: client dropout, partial participation, robust aggregation. 3. Scalability: thousands of clients, asynchronous updates, edge computing integration. Applications: 1. Mobile keyboard: next-word prediction, language modeling, user privacy. 2. Healthcare: medical imaging, cross-institutional collaboration, patient privacy. 3. Financial services: fraud detection, credit scoring, regulatory compliance. Evaluation: convergence analysis, privacy guarantees, communication costs, accuracy vs privacy trade-offs.