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Energy-efficient resource management for federated edge learning with CPU-GPU heterogeneous computing

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Title: Energy-efficient resource management for federated edge learning with CPU-GPU heterogeneous computing
Authors: Zeng, Q
Du, Y
Huang, K
Leung, KK
Item Type: Journal Article
Abstract: Edge machine learning involves the deployment of learning algorithms at the network edge to leverage massive distributed data and computation resources to train artificial intelligence (AI) models. Among others, the framework of federated edge learning (FEEL) is popular for its data-privacy preservation. FEEL coordinates global model training at an edge server and local model training at devices that are connected by wireless links. This work contributes to the energy-efficient implementation of FEEL in wireless networks by designing joint computation-and-communication resource management ( C2 RM). The design targets the state-of-the-art heterogeneous mobile architecture where parallel computing using both CPU and GPU, called heterogeneous computing , can significantly improve both the performance and energy efficiency. To minimize the sum energy consumption of devices, we propose a novel C2 RM framework featuring multi-dimensional control including bandwidth allocation, CPU-GPU workload partitioning and speed scaling at each device, and C2 time division for each link. The key component of the framework is a set of equilibriums in energy rates with respect to different control variables that are proved to exist among devices or between processing units at each device. The results are applied to designing efficient algorithms for computing the optimal C2 RM policies faster than the standard optimization tools. Based on the equilibriums, we further design energy-efficient schemes for device scheduling and greedy spectrum sharing that scavenges “spectrum holes” resulting from heterogeneous C2 time divisions among devices. Using a real dataset, experiments are conducted to demonstrate the effectiveness of C2 RM on improving the energy efficiency of a FEEL system.
Issue Date: 1-Dec-2021
Date of Acceptance: 7-Jun-2021
URI: http://hdl.handle.net/10044/1/96280
DOI: 10.1109/TWC.2021.3088910
ISSN: 1536-1276
Publisher: Institute of Electrical and Electronics Engineers
Start Page: 7947
End Page: 7962
Journal / Book Title: IEEE Transactions on Wireless Communications
Volume: 20
Issue: 12
Keywords: Science & Technology
Technology
Engineering, Electrical & Electronic
Telecommunications
Engineering
Radio resource management
energy-efficient computation and communication
scheduling
federated learning
heterogeneous computing
BANDWIDTH ALLOCATION
Science & Technology
Technology
Engineering, Electrical & Electronic
Telecommunications
Engineering
Radio resource management
energy-efficient computation and communication
scheduling
federated learning
heterogeneous computing
BANDWIDTH ALLOCATION
Networking & Telecommunications
0805 Distributed Computing
0906 Electrical and Electronic Engineering
1005 Communications Technologies
Publication Status: Published
Online Publication Date: 2021-06-21
Appears in Collections:Computing
Electrical and Electronic Engineering



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