DDP-SA

Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregation

arXiv:2604.07125v2 Announce Type: replace Abstract: This article presents DDP-SA, a scalable privacy-preserving federated learning framework that jointly leverages client-side local differential privacy (LDP) and full-threshold additive secret sharing (ASS) for secure aggregation. Unlike existing methods that rely solely on differential privacy or on secure multi-party computation (MPC), DDP-SA integrates both techniques to deliver stronger end-to-end privacy guarantees while remaining...