Federated Recommendation System via Differential Privacy

• URL: http://arxiv.org/abs/2005.06670v2
• Date: Sat, 16 May 2020 04:11:01 GMT
• Title: Federated Recommendation System via Differential Privacy
• Authors: Tan Li, Linqi Song and Christina Fragouli
• Abstract summary: We explore how differential privacy based Upper Confidence Bound (UCB) methods can be applied to multi-agent environments. We provide a theoretical analysis on the privacy and regret performance of the proposed methods.
• Score: 31.0963615274522
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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