Fine-Grained Data Selection for Improved Energy Efficiency of Federated Edge Learning

• Published in: IEEE transactions on network science and engineering Vol. 9; no. 5; pp. 3258 - 3271
• Main Authors: Albaseer, Abdullatif, Abdallah, Mohamed, Al-Fuqaha, Ala, Erbad, Aiman
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Beamforming; Computational modeling; Constraints; Convergence rate; Data models; Data selection; Datasets; Edge computing; Edge Intelligence; Energy consumption; Energy efficiency; Federated Edge Learning (FEEL); Iterative algorithms; Iterative methods; Learning Algorithm; Machine learning; Performance evaluation; Resource allocation; Resource management; Servers; Training

In Federated edge learning (FEEL), energy-constrained devices at the network edge consume significant energy when training and uploading their local machine learning models, leading to a decrease in their lifetime. This work proposes novel solutions for energy-efficient FEEL by jointly considering l...