Privacy-Preserving Communication-Efficient Federated Multi-Armed Bandits
Tan Li, Linqi Song

TL;DR
This paper introduces a novel federated multi-armed bandit algorithm that balances privacy, communication efficiency, and learning performance, with theoretical analysis and empirical validation of the trade-offs involved.
Contribution
It proposes a new privacy-preserving, communication-efficient federated bandit algorithm with theoretical regret analysis across different network structures.
Findings
Trade-offs between regret, privacy, and communication costs are theoretically characterized.
The proposed algorithms achieve effective privacy protection with reduced communication.
Empirical results confirm the theoretical trade-offs and performance benefits.
Abstract
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…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Age of Information Optimization · Advanced Bandit Algorithms Research
