Data-Driven Participant Selection and Bandwidth Allocation for Heterogeneous Federated Edge Learning

Albaseer, Abdullatif; Abdallah, Mohamed; Al-Fuqaha, Ala; Erbad, Aiman

doi:10.36227/techrxiv.19317671.v1

Cited by 2 publications

(1 citation statement)

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“…Studying the deployment of FL over wireless networks from different perspectives has received a lot of attention in the last two years. Focusing on scheduling policies, several EDs scheduling policies [8], [11], [12], [17]- [20] have been proposed in order to reduce the FL's training time, aiming to accelerate the convergence rate while accounting for the limited resources of the wireless edge. Amiri et al, [21] investigated four different scheduling algorithms based on update quantization.…”

Section: Related Workmentioning

confidence: 99%

Balanced Energy Consumption Based on Historical Participation of Resource-Constrained Devices in Federated Edge Learning

Albaseer¹,

Abdallah²,

Al-Fuqaha³

et al. 2022

Preprint

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To appear in IEEE In recent years, Federated Edge Learning has gained interest from both industry and academia for deployment at the wireless network edge. However, some resource-restricted edge devices (EDs) bear more computation and communication loads due to the heterogeneity of data and resources. Several approaches have been proposed in the literature to reduce energy costs by scheduling only a few EDs to complete training tasks based on their energy budgets. Nevertheless, from a practical perspective, the incongruent data distribution cannot be captured, resulting in a biased model for EDs that are frequently selected. Furthermore, the frequently scheduled devices deplete their energy quickly, making them inaccessible. Thus, this paper proposes a novel scheduling policy based on the historical participation of each ED that ensures an unbiased model while balancing learning tasks so that all EDs consume equivalent energy at the end of the training. We formulate an optimization problem based on Jain’s fairness index, followed by tractable algorithms to solve this problem. Extensive experiments have been conducted, and the results show that the proposed algorithm balances the energy consumption among EDs and accelerates the convergence rate while achieving satisfactory performance.

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