提出噪声抵消机制，在部分参与下实现联邦学习的隐私保护与高效收敛。
Privacy-Preserving Federated Convex Optimization: Balancing Partial-Participation and Efficiency via Noise Cancellation
- 设计噪声抵消机制，解决部分设备参与时的隐私保护难题。
- 在同质和异质数据下均达到最优收敛速度，保持计算效率。
- 适合大规模分布式系统中需兼顾隐私与性能的场景。
本文针对联邦学习中仅部分设备参与每轮训练的场景，解决差分隐私（DP）的实现挑战。现有方法在全参与情况下表现良好，但在部分参与时性能下降。本文提出一种新颖的噪声抵消机制，在不牺牲收敛速率和计算效率的前提下实现隐私保护。在随机凸优化（SCO）框架下分析表明，该方法对同质与异质数据分布均能实现最优性能。研究成果拓展了差分隐私在联邦学习中的适用范围，为存在部分参与的分布式系统提供了高效且实用的隐私保护方案。
原文摘要 · Abstract (English)
This paper tackles the challenge of achieving Differential Privacy (DP) in Federated Learning (FL) under partial-participation, where only a subset of the machines participate in each time-step. While previous work achieved optimal performance in full-participation settings, these methods struggled to extend to partial-participation scenarios. Our approach fills this gap by introducing a novel noise-cancellation mechanism that preserves privacy without sacrificing convergence rates or computational efficiency. We analyze our method within the Stochastic Convex Optimization (SCO) framework and show that it delivers optimal performance for both homogeneous and heterogeneous data distributions. This work expands the applicability of DP in FL, offering an efficient and practical solution for privacy-preserving learning in distributed systems with partial participation.
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