Quality- and Availability-Based Device Scheduling and Resource Allocation for Federated Edge Learning

To achieve an efficient federated edge learning (FEEL) system, the scheme of device scheduling and resource allocation should jointly perceive the device availability, wireless channel quality, and local gradient quality. The existing literature on FEEL rarely considers these three aspects simultane...

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Bibliographic Details
Published in:IEEE communications letters 2022-11, Vol.26 (11), p.2626-2630
Main Authors: Wen, Wanli, Zhang, Yi, Chen, Chen, Jia, Yunjian, Luo, Lu, Tang, Lei
Format: Article
Language:English
Subjects:
Availability
channel and gradient quality
Computational modeling
device availability
Federated edge learning
Job shop scheduling
Learning
Performance evaluation
Resource allocation
Resource management
Resource scheduling
Scheduling
Servers
Training
Wireless communication
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Summary:To achieve an efficient federated edge learning (FEEL) system, the scheme of device scheduling and resource allocation should jointly perceive the device availability, wireless channel quality, and local gradient quality. The existing literature on FEEL rarely considers these three aspects simultaneously, so the schemes they proposed still have room for improving the efficiency of FEEL, which motivates our work. In this letter, by mathematically modeling the device availability, wireless channel quality, and gradient quality, and deriving the convergence bound for model training in the FEEL system, we formulate a joint device scheduling and resource allocation problem, aiming to improve the FEEL efficiency. The formulated problem is a challenging non-convex problem. By exploring its structural properties and utilizing the KKT conditions, we obtain an optimal solution in closed-form. The analytical results enable us to gain some important insights into how the device availability, wireless channel quality, and gradient quality affect device scheduling and resource allocation in the FEEL system.
ISSN:1089-7798
1558-2558
DOI:10.1109/LCOMM.2022.3194558