federated-learning

Federated learning engineering covering FedAvg and FedProx aggregation, differential privacy (DP-SGD, Opacus), secure aggregation protocols, PySyft, the Flower framework, split learning, on-device training, communication compression, non-IID data heterogeneity, and model poisoning defenses for privacy-preserving distributed ML.