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.

我们研究了多臂赌博机和线性赌博机的联邦纯探索问题，在该问题中， M 个代理通过与中央服务器通信来合作地识别最佳臂。为了增强算法对延迟和代理不可用性的鲁棒性，我们提出了第一个用于固定置信度的联邦异步多臂赌博机和线性赌博机算法。我们的理论分析表明，所提出的算法在完全异步环境中实现了接近最优的样本复杂度和高效的通信成本。此外，基于合成和现实世界数据的实验结果从经验上阐明了所提出算法的有效性和通信成本效益。