Flooding with Absorption: An Efficient Protocol for Heterogeneous Bandits over Complex Networks

Authors Junghyun Lee , Laura Schmid , Se-Young Yun

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Author Details

Junghyun Lee
  • Kim Jaechul Graduate School of AI, KAIST, Seoul, Republic of Korea
Laura Schmid
  • Kim Jaechul Graduate School of AI, KAIST, Seoul, Republic of Korea
Se-Young Yun
  • Kim Jaechul Graduate School of AI, KAIST, Seoul, Republic of Korea


The authors thank Ulrich Schmid (TU Wien) and the anonymous reviewers for helpful comments and suggestions.

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Junghyun Lee, Laura Schmid, and Se-Young Yun. Flooding with Absorption: An Efficient Protocol for Heterogeneous Bandits over Complex Networks. In 27th International Conference on Principles of Distributed Systems (OPODIS 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 286, pp. 20:1-20:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)


Multi-armed bandits are extensively used to model sequential decision-making, making them ubiquitous in many real-life applications such as online recommender systems and wireless networking. We consider a multi-agent setting where each agent solves their own bandit instance endowed with a different set of arms. Their goal is to minimize their group regret while collaborating via some communication protocol over a given network. Previous literature on this problem only considered arm heterogeneity and networked agents separately. In this work, we introduce a setting that encompasses both features. For this novel setting, we first provide a rigorous regret analysis for a standard flooding protocol combined with the classic UCB policy. Then, to mitigate the issue of high communication costs incurred by flooding in complex networks, we propose a new protocol called Flooding with Absorption (FwA). We provide a theoretical analysis of the resulting regret bound and discuss the advantages of using FwA over flooding. Lastly, we experimentally verify on various scenarios, including dynamic networks, that FwA leads to significantly lower communication costs despite minimal regret performance loss compared to other network protocols.

Subject Classification

ACM Subject Classification
  • Theory of computation → Multi-agent learning
  • Theory of computation → Sequential decision making
  • Theory of computation → Regret bounds
  • Networks → Network protocol design
  • multi-armed bandits
  • multi-agent systems
  • collaborative learning
  • network protocol
  • flooding


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