Privacy-Preserving Communication-Efficient Federated Multi-Armed Bandits

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

10 Scopus Citations
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Original languageEnglish
Pages (from-to)773-787
Number of pages15
Journal / PublicationIEEE Journal on Selected Areas in Communications
Issue number3
Online published12 Jan 2022
Publication statusPublished - Mar 2022


Communication bottleneck and data privacy are two critical concerns in federated multi-armed bandit (MAB) problems, such as situations in decision-making and recommendations of connected vehicles via wireless. In this paper, we design the privacy-preserving communication-efficient algorithm in such problems and study the interactions among privacy, communication and learning performance in terms of the regret. To be specific, we design privacy-preserving learning algorithms and communication protocols and derive the learning regret when networked private agents are performing online bandit learning in a master-worker, a decentralized and a hybrid structure. Our bandit learning algorithms are based on epoch-wise sub-optimal arm eliminations at each agent and agents exchange learning knowledge with the server/each other at the end of each epoch. Furthermore, we adopt the differential privacy (DP) approach to protect the data privacy at each agent when exchanging information; and we curtail communication costs by making less frequent communications with fewer agents participation. By analyzing the regret of our proposed algorithmic framework in the master-worker, decentralized and hybrid structures, we theoretically show trade-offs between regret and communication costs/privacy. Finally, we empirically show these trade-offs which are consistent with our theoretical analysis.

Research Area(s)

  • communication efficient learning, Costs, Data models, differential privacy, Federated learning, Knowledge engineering, multi-armed bandit, Privacy, Protocols, Servers, Wireless communication