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Federated Learning for Energy-balanced Client Selection in Mobile Edge Computing
Ref: CISTER-TR-210305       Publication Date: 28, Jun to 2, Jul, 2021

Federated Learning for Energy-balanced Client Selection in Mobile Edge Computing

Ref: CISTER-TR-210305       Publication Date: 28, Jun to 2, Jul, 2021

Abstract:
Mobile edge computing (MEC) has been considered as a promising technology to provide seamless integration of multiple application services. Federated learning (FL) is carried out at edge clients in MEC for privacy-preserving training of data processing models. Despite that the edge clients with small data payloads consume less energy on FL training, the small data payload gives rise to a low learning accuracy due to insufficient input to the FL training. Inadequate selection of the edge clients can result in a large energy consumption at the edge clients, or a low learning accuracy of the FL training. In this paper, a new FL-based client selection optimization is proposed to balance the trade-off between energy consumption of the edge clients and the learning accuracy of FL. We first show that this optimization problem is NP-complete. Next, we propose a FL-based energy-accuracy balancing heuristic algorithm to approximate the optimal client selection in polynomial time. The numerical results show the advantage of our proposed algorithm.

Authors:
Jingjing Zheng
,
Kai Li
,
Eduardo Tovar
,
Mohsen Guizani


Events:

IWCMC 2021
28, Jun, 2021 >> 2, Jul, 2021
17th International Wireless Communications and Mobile Computing
Harbin, China


17th International Wireless Communications & Mobile Computing Conference (IWCMC 2021).
Harbin, China.

Notes: Jingjing Zheng, Kai Li, Eduardo Tovar, Mohsen Guizani



Record Date: 30, Mar, 2021