Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregation
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 computationally practical. The framework introduces a two-stage protection mechanism: clients first perturb their local gradients with calibrated Laplace noise, then decompose the noisy gradients into additive secret shares that are distributed across multip...
| Authors | Wenjing Wei, Farid Nait-Abdesselam, Alla Jammine |
|---|---|
| Primary category | cs.CR |
| Published | 8 April 2026 |
| Last updated at source | 6 June 2026 |
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