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المؤلفMrad, Ilyes
المؤلفSamara, Lutfi
المؤلفAl-Abbasi, Abubakr
المؤلفHamila, Ridha
المؤلفErbad, Aiman
المؤلفKiranyaz, Serkan
تاريخ الإتاحة2023-04-04T09:09:09Z
تاريخ النشر2022
اسم المنشورIEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1109/PIMRC54779.2022.9977962
معرّف المصادر الموحدhttp://hdl.handle.net/10576/41646
الملخصFederated 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.
راعي المشروعVII. 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.
اللغةen
الناشرIEEE
الموضوعEnergy
Fairness
Federated learning
Non-Orthogonal Multiple Access (NOMA)
العنوانFederated Learning in NOMA Networks: Convergence, Energy and Fairness-Based Design
النوعConference Paper
الصفحات975-981
رقم المجلد2022-September
dc.accessType Abstract Only


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