Show simple item record

AuthorAzmy, Sherif B.
AuthorAbutuleb, Amr
AuthorSorour, Sameh
AuthorZorba, Nizar
AuthorHassanein, Hossam S.
Available date2024-05-02T11:19:26Z
Publication Date2021
Publication NameIEEE International Conference on Communications
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/ICC42927.2021.9500304
ISSN15503607
URIhttp://hdl.handle.net/10576/54557
AbstractFederated Learning (FL) is a novel distributed learning paradigm in which local learning models are simultaneously trained using the stored data on multiple devices, then ultimately aggregated into a global model. A promising use case of FL is the training of a global model using the data collected by unmanned aerial vehicles (UAVs) during their flight, which is invaluable in scenarios in which an infrastructure cannot be accessed (e.g., disaster). However, this is challenging as limited resources are to be distributed between flight time, sensing, processing, and communication. In this paper, we address the resource problem for a set of heterogeneous UAVs with different computation and communication capabilities from distributed point of view. We propose the usage of Device-to-Device (D2D) communication to fairly distribute the data so-far collected by UAVs with different capabilities by posing it as an optimal transport problem. Our contribution is two-fold: (1) We obtain the fairest distribution of data given the UAVs' computational capabilities such that global learning time is minimal; (2) We devise a scheme using Optimal Transport (OT) to achieve such a fair distribution between UAVs. The performance of the proposed techniques is demonstrated in an FL setting with different UAV topologies with the FL training done using the MNIST dataset.
SponsorACKNOWLEDGMENT This research is supported by a grant from the Natural Sciences and Engineering Research Council of Canada (NSERC) under grant number ALLRP 549919-20.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectDistributed Learning
Federated Learning
Flying Ad-Hoc Networks
Mobile Edge Computing
Optimal Transport
Unmanned Aerial Vehicles
TitleOptimal Transport for UAV D2D Distributed Learning: Example using Federated Learning
TypeConference Paper
dc.accessType Abstract Only


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record