Statistical Limits and Efficient Algorithms for Differentially Private Federated Learning

In three linesStudy of trade-offs between estimation accuracy, differential privacy, and communication cost in federated learning. Proposes FedHybrid and FedNewton, improvements over FedAvg and FedSGD with finite-sample MSE upper bounds and minimax lower bounds. Evaluation on logistic regression and neural networks (MNIST, CIFAR-10).

Summary generated by Claude — human-verified