عرض بسيط للتسجيلة

المؤلفMrad, Ilyes
المؤلفHamila, Ridha
المؤلفErbad, Aiman
المؤلفGabbouj, Moncef
تاريخ الإتاحة2023-04-04T09:09:09Z
تاريخ النشر2023
اسم المنشورPervasive and Mobile Computing
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1016/j.pmcj.2022.101739
معرّف المصادر الموحدhttp://hdl.handle.net/10576/41643
الملخصOver 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
راعي المشروعThis 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.
اللغةen
الناشرElsevier
الموضوعAccess fairness
Energy
Federated Learning
Model accuracy
Non-orthogonal multiple access (NOMA)
العنوانJoint learning and optimization for Federated Learning in NOMA-based networks
النوعArticle
رقم المجلد89
dc.accessType Abstract Only


الملفات في هذه التسجيلة

الملفاتالحجمالصيغةالعرض

لا توجد ملفات لها صلة بهذه التسجيلة.

هذه التسجيلة تظهر في المجموعات التالية

عرض بسيط للتسجيلة