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In this paper, we are interested in what we term the federated private bandits framework, that combines differential privacy with multi-agent bandit learning.
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J. Rosenski, O. Shamir, and L. Szlak, “Multi-player bandits–a musical chairs approach,” in International Conference on Machine Learning , 2016, pp. 155–163
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M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security . ACM, 2016, pp. 308–318
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A. C. Y. Tossou and C. Dimitrakakis, “Algorithms for differentially private multi-armed bandits,” in Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence , ser. AAAI’16. AAAI Press, 2016, p. 2087–2093
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