Federated Recommendation System via Differential Privacy

Tan Li; Linqi Song; Christina Fragouli

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arXiv · 2005.06670

Federated Recommendation System via Differential Privacy

Tan Li·Linqi Song·Christina Fragouli

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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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Tan Li, Linqi Song, Christina Fragouli. 2020-05-14. Federated Recommendation System via Differential Privacy. https://arxiv.org/abs/2005.06670

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