FedNSA: Boosting Secure Aggregation by Assembling Differentially Private Noise Shares

Summary

The paper proposes FedNSA, a secure aggregation protocol for federated learning that combines differential privacy with encryption and multi-party computation. Differential privacy noise obfuscates each client's model update, encryption correlates the noise across clients, and MPC cancels it exactly at the server. The authors claim lower communication and computation overhead than existing encryption- and MPC-based methods, and report experiments on public datasets against state-of-the-art baselines.