论文标题
通过差异隐私联合推荐系统
Federated Recommendation System via Differential Privacy
论文作者
论文摘要
在本文中，我们对联合私人土匪框架的名称感兴趣，该框架将差异隐私与多代理匪徒学习相结合。我们探索如何将基于差异隐私的上限限制（UCB）方法应用于多区域环境，尤其是在“大师工作者”和“完全分散”设置中的联合学习环境中。我们提供了有关拟议方法的隐私和遗憾绩效的理论分析，并探讨了这两者之间的权衡。
In this paper, we are interested in what we term the federated private bandits framework, that combines differential privacy with multi-agent bandit learning. We explore how differential privacy based Upper Confidence Bound (UCB) methods can be applied to multi-agent environments, and in particular to federated learning environments both in `master-worker' and `fully decentralized' settings. We provide a theoretical analysis on the privacy and regret performance of the proposed methods and explore the tradeoffs between these two.
