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）和全阈值加法秘密共享（ASS）实现安全聚合。与仅依赖差分隐私或仅依赖安全多方计算（MPC）的现有方法不同，DDP-SA整合两种技术以提供更强的端到端隐私保障，同时保持计算实用性。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，随后将噪声梯度分解为加法秘密份额，并分发至多个中间服务器。这种设计确保：（i）任何单个被攻破的服务器或通信信道均无法泄露客户端更新的任何信息；（ii）参数服务器仅重构聚合后的噪声梯度，绝不获取任何客户端特有贡献。大量实验表明，DDP-SA在实现比独立LDP显著更高的模型准确性的同时，提供比纯MPC方法更强的隐私保护。所提框架与参与者数量线性扩展，为计算和通信开销可控的联邦学习应用提供了实用的隐私保护解决方案。