提出分布式隐私优化的完整权衡理论，同时兼顾精度、通信与隐私。
Characterizing the Accuracy-Communication-Privacy Trade-off in Distributed Stochastic Convex Optimization
- 基于Vaidya平面切割法设计新算法，实现最优平衡。
- 首次给出精度-通信-隐私三者权衡的严格下界与上界匹配。
- 适合关注分布式机器学习隐私保护的研究者参考。
我们研究在分布式设置下具有M个客户端的差分隐私随机凸优化（DP-SCO）问题，每个客户端拥有N个来自底层数据分布的独立同分布样本。目标是在保证本地数据隐私的前提下，通过跨M个客户端的协作最小化凸种群损失。本文系统分析了该问题中的精度-通信-隐私权衡，提出了新的下界证明方法和基于Vaidya平面切割法的新型算法，实现了上界与下界的完全匹配。因此，本工作对分布式环境下DP-SCO的精度-通信-隐私权衡提供了完整刻画。
原文摘要 · Abstract (English)
We consider the problem of differentially private stochastic convex optimization (DP-SCO) in a distributed setting with $M$ clients, where each of them has a local dataset of $N$ i.i.d. data samples from an underlying data distribution. The objective is to design an algorithm to minimize a convex population loss using a collaborative effort across $M$ clients, while ensuring the privacy of the local datasets. In this work, we investigate the accuracy-communication-privacy trade-off for this problem. We establish matching converse and achievability results using a novel lower bound and a new algorithm for distributed DP-SCO based on Vaidya's plane cutting method. Thus, our results provide a complete characterization of the accuracy-communication-privacy trade-off for DP-SCO in the distributed setting.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。