Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks
Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks
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DOI：
10.1109/twc.2021.3113346
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发表时间：
2021-01
影响因子：
10.4
通讯作者：
Jie Xu;Heqiang Wang;Lixing Chen
中科院分区：
文献类型：
--
作者：
Jie Xu;Heqiang Wang;Lixing Chen
本文研究了联合学习（FL）系统，其中多个FL服务在无线网络中共存并共享常见的无线资源。它填充了现有文献中多个同时提供的FL服务的无线资源分配的空白。我们的方法设计了一个两级资源分配框架，其中包括服务内资源分配和服务间资源分配。服务内资源分配问题旨在通过优化每种FL服务客户的带宽分配来最大程度地降低FL的长度。基于此，进一步考虑了服务间资源分配问题，该问题将在多个同时提供的FL服务之间分配带宽资源。我们考虑FL服务的合作和自私提供者。对于合作FL服务提供商，我们设计了分布式的带宽分配算法，以优化多个FL服务的整体性能，同时迎合FL服务和客户隐私之间的公平性。对于自私的FL服务提供商，新的拍卖计划是由FL服务提供商设计为竞标者和网络运营商作为拍卖师的。设计的拍卖计划在整体FL性能与公平性之间取得了平衡。我们的仿真结果表明，在各种网络条件下，提出的算法优于其他基准。
This paper studies a federated learning (FL) system, where 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 intra-service resource allocation and 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 catering it 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 providers as the bidders and the network operator 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.