Energy-Efficient Resource Management for Federated Edge Learning With CPU-GPU Heterogeneous Computing
| Autor: | Kin K. Leung, Qunsong Zeng, Yuqing Du, Kaibin Huang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Technology Edge device Computer science Computer Science - Information Theory Distributed computing Radio resource management Symmetric multiprocessor system energy-efficient computation and communication 0805 Distributed Computing Engineering Mobile architecture FOS: Electrical engineering electronic engineering information engineering 1005 Communications Technologies scheduling Electrical Engineering and Systems Science - Signal Processing Electrical and Electronic Engineering Science & Technology federated learning Wireless network Information Theory (cs.IT) Applied Mathematics Engineering Electrical & Electronic Energy consumption heterogeneous computing Computer Science Applications 0906 Electrical and Electronic Engineering Bandwidth allocation BANDWIDTH ALLOCATION Telecommunications Enhanced Data Rates for GSM Evolution Networking & Telecommunications Efficient energy use |
| Zdroj: | IEEE Transactions on Wireless Communications. 20:7947-7962 |
| ISSN: | 1558-2248 1536-1276 |
| DOI: | 10.1109/twc.2021.3088910 |
| Popis: | 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 edge 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 ($\text{C}^2$RM). The design targets the state-of-the-art heterogeneous mobile architecture where parallel computing using both a CPU and a 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 $\text{C}^2$RM framework featuring multi-dimensional control including bandwidth allocation, CPU-GPU workload partitioning and speed scaling at each device, and $\text{C}^2$ 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 $\text{C}^2$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 $\text{C}^2$ time divisions among devices. Using a real dataset, experiments are conducted to demonstrate the effectiveness of $\text{C}^2$RM on improving the energy efficiency of a FEEL system. |
| Databáze: | OpenAIRE |
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