This paper studies a federated learning (FL) system, where \textit{multiple} FL services co-exist in a wireless network and share common wireless resources. It fills the void of wireless resource allocation for multiple simultaneous FL services in the existing literature. Our method designs a two-level resource allocation framework comprising \emph{intra-service} resource allocation and \emph{inter-service} resource allocation. The intra-service resource allocation problem aims to minimize the length of FL rounds by optimizing the bandwidth allocation among the clients of each FL service. Based on this, an inter-service resource allocation problem is further considered, which distributes bandwidth resources among multiple simultaneous FL services. We consider both cooperative and selfish providers of the FL services. For cooperative FL service providers, we design a distributed bandwidth allocation algorithm to optimize the overall performance of multiple FL services, meanwhile cater to the fairness among FL services and the privacy of clients. For selfish FL service providers, a new auction scheme is designed with the FL service owners as the bidders and the network provider as the auctioneer. The designed auction scheme strikes a balance between the overall FL performance and fairness. Our simulation results show that the proposed algorithms outperform other benchmarks under various network conditions.
翻译:本文研究一个联合学习(FL)系统,在这个系统中,FL服务在一个无线网络中共同存在,并共享共同的无线资源。它填补了现有文献中多个同步FL服务的无线资源分配空白。我们的方法设计了一个两级资源分配框架,包括\emph{intra-Service}资源分配和\emph{inter-Service}资源分配。服务内部资源分配问题的目的是通过优化每个FL服务的客户之间的带宽分配,最大限度地减少FL回合的长度。在此基础上,进一步审议了一个服务间资源分配问题,在多个同时存在的FL服务之间分配带宽资源。我们考虑的是FL服务的合作性和自私性提供方。对于FL服务的合作性服务提供者,我们设计了一个分布式带宽分配算法,以优化多种FL服务的总体性能和客户的隐私。对于自私的FL服务供应商来说,与FL服务所有人作为投标人和网络提供者作为拍卖商设计了一个新的拍卖计划。我们设计的拍卖计划在FL服务供应商和网络的多个同时提供者之间形成了一种公平性标准。我们所设计的拍卖计划在模拟的网络中形成了一种平衡,以显示我们整个FL的公平性模型中的各种结果。