论文标题
联合X武器匪徒
Federated X-Armed Bandit
论文作者
论文摘要
这项工作建立了联合$ \ Mathcal {x} $ - 武装强盗的第一个框架，在该域中，不同的客户面对在同一域上定义的异质本地目标功能，并且需要协作确定全局最佳选择。我们为此类问题提出了第一个联合算法，名为\ texttt {fed-pne}。通过利用层次分区内的全球目标的拓扑结构和弱平滑性属性，我们的算法就客户数量和评估预算都实现了统一的累积后悔。同时，它仅需要中央服务器和客户端之间的对数通信，以保护客户端隐私。关于合成功能和实际数据集的实验结果验证了\ texttt {fed-pne}的优势比各种集中式和联合基线算法。
This work establishes the first framework of federated $\mathcal{X}$-armed bandit, where different clients face heterogeneous local objective functions defined on the same domain and are required to collaboratively figure out the global optimum. We propose the first federated algorithm for such problems, named \texttt{Fed-PNE}. By utilizing the topological structure of the global objective inside the hierarchical partitioning and the weak smoothness property, our algorithm achieves sublinear cumulative regret with respect to both the number of clients and the evaluation budget. Meanwhile, it only requires logarithmic communications between the central server and clients, protecting the client privacy. Experimental results on synthetic functions and real datasets validate the advantages of \texttt{Fed-PNE} over various centralized and federated baseline algorithms.
