Work

Training efficiency optimization algorithm of wireless federated learning based on processor performance and network condition awareness

Year: 2024

Type: article

Abstract: With the explosive growth of smart mobile devices in wireless networks, the increasing computational power of mobile chips and the growing concern for personal privacy, a decentralized deep learning framework at the mobile terminal layer has emerged called federated learning (FL) to enhance user experience. This paper studies the training efficiency optimization problem of wireless FL that jointly considers processor performance, channel conditions and terminals’ power in a non-independent identically distribution (non-IID) scenario. And, the training efficiency optimization problem is mathematically modeled and then decomposed into several sub-problems based on the independence and decoupling of the variables involved. To enhance the training efficiency of wireless FL, a comprehensive scheduling strategy encompassing computational and communication aspects is proposed. Simulation results show that the proposed scheduling strategy for wireless FL achieves superior learning performance with reduced training latency.

Source: EURASIP Journal on Advances in Signal Processing

DOI: 10.1186/s13634-024-01192-6

Authors: Guohao Pang, Xiaorong Zhu

Institution: Nanjing University of Posts and Telecommunications

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,790

Funders: National Natural Science Foundation of China, Government of Jiangsu Province, Natural Science Foundation of Jiangsu Province, Jiangsu Provincial Key Research and Development Program