English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

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 FLservices in the existing literature. Our method designs a two-level resourceallocation framework comprising \emph{intra-service} resource allocation and\emph{inter-service} resource allocation. The intra-service resource allocationproblem aims to minimize the length of FL rounds by optimizing the bandwidthallocation among the clients of each FL service. Based on this, aninter-service resource allocation problem is further considered, whichdistributes bandwidth resources among multiple simultaneous FL services. Weconsider both cooperative and selfish providers of the FL services. Forcooperative FL service providers, we design a distributed bandwidth allocationalgorithm 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 FLservice owners as the bidders and the network provider as the auctioneer. Thedesigned auction scheme strikes a balance between the overall FL performanceand fairness. Our simulation results show that the proposed algorithmsoutperform other benchmarks under various network conditions.

Bandwidth Allocation for Multiple Federated Learning Services in Wireless Edge Networks