Joint Topology and Computation Resource Optimization for Federated Edge Learning

Huang，Shanfeng1,2,3; Wang，Shuai1; Wang，Rui1,3; Huang，Kaibin2

10.1109/GCWkshps52748.2021.9682096

2021

978-1-6654-2391-5

2021 IEEE Globecom Workshops, GC Wkshps 2021 - Proceedings

1-6

7-11 Dec. 2021

Madrid, Spain

Federated edge learning (FEEL) is envisioned as a promising paradigm to achieve privacy-preserving distributed learning. However, it consumes excessive learning time due to the existence of straggler devices caused by the heterogeneity of wireless channels and edge devices' resources. In this paper, a novel topology-optimized federated edge learning (TOFEL) scheme is proposed to tackle the heterogeneity issue in federated learning, so as to improve the communication-and-computation efficiency. Specifically, a problem of jointly optimizing the gradient aggregation topology and computing speed is formulated to minimize the weighted summation of energy consumption and latency. To solve the mixed-integer nonlinear problem, we propose a novel penalty-based successive convex approximation method, which converges to a stationary point of the primal problem under mild conditions. Simulation results demonstrate that the proposed TOFEL scheme remarkably accelerates the federated learning process, and achieves a higher energy efficiency.

Wireless communication Energy consumption Computer aided instruction Distance learning Simulation Conferences Collaborative work

第一

英语

EI

20221111786082

Energy efficiency ; Energy utilization ; Privacy-preserving techniques

Energy Conservation:525.2 ; Energy Utilization:525.3 ; Telecommunication; Radar, Radio and Television:716 ; Telephone Systems and Related Technologies; Line Communications:718 ; Data Processing and Image Processing:723.2 ; Combinatorial Mathematics, Includes Graph Theory, Set Theory:921.4

2-s2.0-85126104189

Scopus

1.Southern University of Science and Technology (SUSTech),Department of Electrical and Electronic Engineering,China
2.Department of Electrical and Electronic Engineering,The University of Hong Kong,Hong Kong
3.The University Key Laboratory of Advanced Wireless Communications of Guangdong Province,SUSTech,China

Huang，Shanfeng,Wang，Shuai,Wang，Rui,et al. Joint Topology and Computation Resource Optimization for Federated Edge Learning[C],2021:1-6.