Hierarchical Federated Learning over HetNets enabled by Wireless Energy Transfer
المؤلف | Hamdi, Rami |
المؤلف | Said, Ahmed Ben |
المؤلف | Erbad, Aiman |
المؤلف | Mohamed, Amr |
المؤلف | Hamdi, Mounir |
المؤلف | Guizani, Mohsen |
تاريخ الإتاحة | 2022-11-09T20:26:31Z |
تاريخ النشر | 2021-01-01 |
اسم المنشور | 2021 IEEE Global Communications Conference, GLOBECOM 2021 - Proceedings |
المعرّف | http://dx.doi.org/10.1109/GLOBECOM46510.2021.9685344 |
الاقتباس | Hamdi, R., Said, A. B., Erbad, A., Mohamed, A., Hamdi, M., & Guizani, M. (2021, December). Hierarchical Federated Learning over HetNets enabled by Wireless Energy Transfer. In 2021 IEEE Global Communications Conference (GLOBECOM) (pp. 01-06). IEEE. |
الترقيم الدولي الموحد للكتاب | 9781728181042 |
الملخص | Training centralized machine learning (ML) models becomes infeasible in wireless networks due to the increasing number of internet of things (IoT) and mobile devices and the prevalence of the learning algorithms to adapt tasks in dynamic situations with heterogeneous networks (HetNets) and battery limited devices. Hierarchical federated learning (HFL) has been proposed as a promising learning that can preserve the data privacy of the wireless devices, tackle the communication bottlenecks in wireless networks, and improve the energy effi-ciency. We propose a novel energy-efficient HFL framework for HetNets with massive multiple-input multiple-output (MIMO) wireless backhaul enabled by wireless energy transfer (WET). We formulate a joint energy management and device association optimization problem in HFL over HetNets subject to maximal divergence constraints. Next, an optimal solution is developed, but with high complexity. To reduce the complexity, a heuristic algorithm for HFL over HetNets with energy, channel quality, and accuracy constraints, is developed in order to minimize the grid energy consumption cost and preserve the value of loss function, which captures the HFL performance. Simulation results show the efficiency of the proposed resource management approach in the HFL context in terms of grid power consumption cost and training loss. |
راعي المشروع | This work was made possible by NPRP-Standard (NPRP-S) Thirteen (13th) Cycle grant # NPRP13S-0205-200265 from the Qatar National Research Fund (a member of Qatar Foundation). The findings herein reflect the work, and are solely the responsibility, of the authors. |
اللغة | en |
الناشر | Institute of Electrical and Electronics Engineers Inc. |
الموضوع | device association energy efficiency HetNets Hierarchical federated learning |
النوع | Conference |
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