Collaborative Mean Estimation over Intermittently Connected Networks with Peer-To-Peer Privacy
Collaborative Mean Estimation over Intermittently Connected Networks with Peer-To-Peer Privacy
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DOI：
10.1109/isit54713.2023.10206910
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发表时间：
2023-02
期刊：
影响因子：
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通讯作者：
R. Saha;Mohamed Seif;M. Yemini;A. Goldsmith;H. Poor
中科院分区：
文献类型：
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作者：
R. Saha;Mohamed Seif;M. Yemini;A. Goldsmith;H. Poor
这项工作考虑的问题，分布式均值估计（DME）在网络与间歇性连接，其目标是学习一个全球性的统计数据样本本地化的分布节点的帮助下，一个中央服务器。为了减轻间歇性链接的影响，节点可以与邻居合作计算本地共识，并将其转发到中央服务器。在这样的设置中，任何一对节点之间的通信必须满足局部差分隐私约束。我们研究了协作中继和隐私泄漏之间的权衡，由于节点之间的额外数据共享，随后，提出了一种新的差分隐私协作算法DME，以实现最佳的权衡。最后，我们提出了数值模拟，以证实我们的理论研究结果。
This work considers the problem of Distributed Mean Estimation (DME) over networks with intermittent connectivity, where the goal is to learn a global statistic over the data samples localized across distributed nodes with the help of a central server. To mitigate the impact of intermittent links, nodes can collaborate with their neighbors to compute local consensus which they forward to the central server. In such a setup, the communications between any pair of nodes must satisfy local differential privacy constraints. We study the tradeoff between collaborative relaying and privacy leakage due to the additional data sharing among nodes and, subsequently, propose a novel differentially private collaborative algorithm for DME to achieve the optimal tradeoff. Finally, we present numerical simulations to substantiate our theoretical findings.