Energy and Gradient Aware Dynamic Scheduling for V2V aided Federated Edge Learning

Federated edge learning (FEEL) is significant for effective machine learning (ML). However, the scarce communication resources and energy-limited vehicles result in part of the local gradient being available for model updates. A residual feedback mechanism is designed to fully use the local gradient...

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Bibliographic Details
Published in:	IEEE communications letters 2024-02, Vol.28 (2), p.1-1
Main Authors:	Guo, Shiqian, Hu, Bin-Jie
Format:	Article
Language:	eng
Subjects:	Algorithms Bandwidth Convergence dynamic scheduling Energy budget Energy limitation Federated edge learning Lyapunov optimization Machine learning residual feedback Servers Task analysis Training Upper bound V2V Vehicular ad hoc networks
Online Access:	Get full text
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Summary:	Federated edge learning (FEEL) is significant for effective machine learning (ML). However, the scarce communication resources and energy-limited vehicles result in part of the local gradient being available for model updates. A residual feedback mechanism is designed to fully use the local gradients. To enable more vehicles to upload the local gradients successfully under a given energy budget, vehicle-to-vehicle (V2V) is applied to explore the energy of neighbors and improve the communication condition to enhance the uploading. Then, the Lyapunov optimization method is employed. Simulation results show that the learning performance of the proposed algorithm outperforms other existing schemes.
ISSN:	1089-7798 1558-2558