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 multiple intermediate servers. This design ensures that (i) no single compromised server or communication channel can reveal any information about individual client updates, and (ii) the parameter server reconstructs only the aggregated noisy gradient, never any client-specific contribution. Extensive experiments show that DDP-SA achieves substantially higher model accuracy than standalone LDP while providing stronger privacy protection than MPC-only approaches. The proposed framework scales linearly with the number of participants and offers a practical, privacy-preserving solution for federated learning applications with controllable computational and communication overhead.

翻译：本文提出DDP-SA，一种可扩展的隐私保护联邦学习框架，该框架联合利用客户端本地差分隐私（LDP）与全阈值加法秘密共享（ASS）实现安全聚合。与现有仅依赖差分隐私或安全多方计算（MPC）的方法不同，DDP-SA集成两种技术以在保持计算可行性的同时提供更强的端到端隐私保障。该框架引入两阶段保护机制：客户端首先使用校准拉普拉斯噪声扰动本地梯度，随后将含噪梯度分解为加法秘密共享份额，并分布至多个中间服务器。此设计确保：（i）任何单一受陷服务器或通信信道均无法泄露单个客户端更新的信息；（ii）参数服务器仅重构聚合后的含噪梯度，绝不获取任何客户端特定贡献。大量实验表明，DDP-SA在模型精度上显著优于独立LDP方法，同时提供比纯MPC方法更强的隐私保护。所提框架随参与者数量线性扩展，为联邦学习应用提供一种计算与通信开销可控的实用化隐私保护解决方案。