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AuthorMrad, Ilyes
AuthorHamila, Ridha
AuthorErbad, Aiman
AuthorGabbouj, Moncef
Available date2023-04-04T09:09:09Z
Publication Date2023
Publication NamePervasive and Mobile Computing
ResourceScopus
URIhttp://dx.doi.org/10.1016/j.pmcj.2022.101739
URIhttp://hdl.handle.net/10576/41643
AbstractOver the past decade, the usage of machine learning (ML) techniques have increased substantially in different applications. Federated Learning (FL) refers to collaborative techniques that avoid the exchange of raw data between the nodes in a distributed training task. This addresses important issues such as data privacy, energy consumption, and the limited availability of clean spectral slots. In this work, we investigate the performance of FL updates with edge devices connected to a leading device (LD) with practical wireless links, where uplink updates from the edge devices to the LD are shared without orthogonalizing the resources. In particular, we adopt a non-orthogonal multiple access (NOMA) uplink scheme, and analytically investigate its effect on the convergence round (CR) and the accuracy of the FL model. Moreover, we formulate an optimization problem that aims at minimizing the CR, and further guarantees communication fairness between the users while considering the per-device energy consumption figures and the accuracy of the realized global FL model. Monte-Carlo simulations prove the reliability of our derived analytical expressions and reveal the importance of adopting a joint optimization approach that demonstrates a significant reduction in communication latency, while taking into account user fairness in the NOMA network, improving the energy consumption figures and yielding acceptable accuracy levels when compared with several baselines. 2022
SponsorThis paper was made possible by Qatar University Internal Grant QUHI-CENG-21/22-3 . The statements made herein are solely the responsibility of the authors.
Languageen
PublisherElsevier
SubjectAccess fairness
Energy
Federated Learning
Model accuracy
Non-orthogonal multiple access (NOMA)
TitleJoint learning and optimization for Federated Learning in NOMA-based networks
TypeArticle
Volume Number89
dc.accessType Abstract Only


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