在隐私保护下，实现通信受限的在线个性化均值估计。
Communication-Constrained Private Decentralized Online Personalized Mean Estimation
- 基于共识算法，在差分隐私框架下协作估计均值。
- 理论证明合作收敛快于本地独立估计，条件是隐私级别和连通性可控。
- 适合分布式在线学习中需保护隐私且带通信限制的场景。
我们研究在多个智能体持续接收未知特定分布数据的环境下，通信受限且需满足隐私约束的协同个性化均值估计问题。在差分隐私框架下，提出一种基于共识的算法，并对任意有界未知分布的数据进行理论收敛分析。结果表明，在理想决策规则及隐私水平与智能体连通性受限的条件下，协作方式比完全本地化方法收敛更快，验证了在在线设置中私密协作的优势。理论结果得到多个数值实验的支持。
原文摘要 · Abstract (English)
We consider the problem of communication-constrained collaborative personalized mean estimation under a privacy constraint in an environment of several agents continuously receiving data according to arbitrary unknown agent-specific distributions. A consensus-based algorithm is studied under the framework of differential privacy in order to protect each agent's data. We give a theoretical convergence analysis of the proposed consensus-based algorithm for any bounded unknown distributions on the agents' data, showing that collaboration provides faster convergence than a fully local approach where agents do not share data, under an oracle decision rule and under some restrictions on the privacy level and the agents' connectivity, which illustrates the benefit of private collaboration in an online setting under a communication restriction on the agents. The theoretical faster-than-local convergence guarantee is backed up by several numerical results.
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