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AuthorMrad, Ilyes
AuthorSamara, Lutfi
AuthorAl-Abbasi, Abubakr
AuthorHamila, Ridha
AuthorErbad, Aiman
AuthorKiranyaz, Serkan
Available date2023-04-04T09:09:09Z
Publication Date2022
Publication NameIEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC
ResourceScopus
URIhttp://dx.doi.org/10.1109/PIMRC54779.2022.9977962
URIhttp://hdl.handle.net/10576/41646
AbstractFederated Learning (FL) is a collaborative machine learning (ML) approach, where different nodes in a network contribute to learning the model parameters. In addition, FL provides several attractive features such as data privacy and energy efficiency. Due to its collaborative nature, model parameters among nodes should be efficiently exchanged, while considering the scarce availability of clean spectral slots. In this work, we propose low-power efficient algorithms for FL of model parameters updates. We consider mobile edge nodes connected to a leading node (LD) with practical wireless links, where uplink updates from the nodes to the LD are shared without orthogonalizing the resources. In particular, we adopt a non-orthogonal multiple access (NOMA) uplink scheme, and investigate its effect on the convergence round (CR) of the model updates. Through deriving an analytical expression of the CR, we leverage it to formulate an optimization problem to minimize the total number of communication rounds and maximize the communication fairness among the nodes. We further investigate the performance of our proposed algorithms by considering different factors, including limited per-node energy and node heterogeneity. Monte-Carlo simulations are used to verify the accuracy of our derived expression of the CR. Moreover, through comprehensive simulation, we show that our proposed schemes largely reduce the communication latency between the LD and the nodes, and improve the communication fairness among the nodes. 2022 IEEE.
SponsorVII. ACKNOWLEDGEMENT This paper was made possible by PDRA grant #5-0424-19005 from the Qatar National Research Fund (a member of Qatar Foundation) and the Qatar University Internal Grant QUHI-CENG-21/22-3. The statements made herein are solely the responsibility of the authors.
Languageen
PublisherIEEE
SubjectEnergy
Fairness
Federated learning
Non-Orthogonal Multiple Access (NOMA)
TitleFederated Learning in NOMA Networks: Convergence, Energy and Fairness-Based Design
TypeConference Paper
Pagination975-981
Volume Number2022-September
dc.accessType Abstract Only


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