Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks

Jie Xu, Heqiang Wang, Lixing Chen

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
JournalIEEE Transactions on Wireless Communications
StateAccepted/In press - 2021


  • Bandwidth
  • Channel allocation
  • Data models
  • Resource management
  • Servers
  • Training
  • Wireless networks

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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