Title: A General Differentially Private Learning Framework for Decentralized Data

Abstract:Decentralized consensus learning has been hugely successful that minimizing a finite sum of expected objectives over a network of agents. However, the local communication across neighbouring agents in the network may lead to the leakage of private information. To address this challenge, we propose a general differentially private (DP) learning framework that is applicable to direct and indirect communication networks without a central coordinator. We show that the proposed algorithm retains the performance guarantee in terms of generalization and finite sample performance. We investigate the impact of local privacy-preserving computation on the global DP guarantee. Further, we extend the discussion by adopting a new class of noise-adding DP mechanisms based on generalized Gaussian distributions to improve the utility-privacy trade-offs. Our numerical results demonstrate the effectiveness of our algorithm and its better performance over the state-of-the-art baseline methods in various decentralized settings.

报告题目：去中心化下保护数据隐私的学习框架

报告人：王亚飞（艾塞克斯大学）

报告时间：2022年12月9日17:00

报告地点：388-974-689

主办单位：新京葡萄威尼斯

报告对象：新京葡萄威尼斯及全校感兴趣的教师、研究生和本科生

内容摘要：去中心化学习通过极小化若干个服务器的损失总和受到广泛研究，但是服务器之间的信息传递会导致个人信息泄漏。我们提出一种可以保护数据隐私的学习框架，并且研究了所提算法的性质---泛化性，有限样本性质。此外，我们理论上给出了全局的隐私保护怎样受局部服务器的影响。考虑到所提算法的可行性，基于广义高斯分布，我们提出了一种新的加噪声的机制。

报告人简介：

王亚飞，英国University of Essex，Assistant Professor，博士生导师。主要研究方向是复杂数据分析特别是影像数据分析， 随机优化。多项研究成果发表在JMVA, CSDA、NeurIPS, AAAI等国际顶级期刊。