Privacy-Preserving Communication-Efficient Federated Multi-Armed Bandits
- URL: http://arxiv.org/abs/2111.01570v1
- Date: Tue, 2 Nov 2021 12:56:12 GMT
- Title: Privacy-Preserving Communication-Efficient Federated Multi-Armed Bandits
- Authors: Tan Li, Linqi Song
- Abstract summary: Communication bottleneck and data privacy are two critical concerns in federated multi-armed bandit (MAB) problems.
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.
- Score: 17.039484057126337
- License: http://creativecommons.org/licenses/by/4.0/
- 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 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 tradeoffs between regret and communication costs/privacy.
Finally, we empirically show these trade-offs which are consistent with our
theoretical analysis.
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