Computer Science > Machine Learning
[Submitted on 17 Oct 2023 (v1), last revised 30 Sep 2024 (this version, v2)]
Title:Pure Exploration in Asynchronous Federated Bandits
View PDFAbstract:We study the federated pure exploration problem of multi-armed bandits and linear bandits, where $M$ agents cooperatively identify the best arm via communicating with the central server. To enhance the robustness against latency and unavailability of agents that are common in practice, we propose the first federated asynchronous multi-armed bandit and linear bandit algorithms for pure exploration with fixed confidence. Our theoretical analysis shows the proposed algorithms achieve near-optimal sample complexities and efficient communication costs in a fully asynchronous environment. Moreover, experimental results based on synthetic and real-world data empirically elucidate the effectiveness and communication cost-efficiency of the proposed algorithms.
Submission history
From: Zichen Wang [view email][v1] Tue, 17 Oct 2023 06:04:00 UTC (613 KB)
[v2] Mon, 30 Sep 2024 00:21:39 UTC (664 KB)
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