Air-Ground Integrated Low-Energy Federated Learning for Secure 6G Communications

doi:10.12142/ZTECOM.202204005

ZTE Communications ›› 2022, Vol. 20 ›› Issue (4): 32-40.DOI: 10.12142/ZTECOM.202204005

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Air-Ground Integrated Low-Energy Federated Learning for Secure 6G Communications

WANG Pengfei¹(), SONG Wei¹, SUN Geng^2,³, WEI Zongzheng¹, ZHANG Qiang¹

^1.School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
^2.School of Computer Science and Technology, Jilin University, Changchun 130015, China
^3.Key Laboratory of Symbolic Computing and Knowledge Engineering, Jilin University, Changchun 130015, China

Received:2022-09-09 Online:2022-12-31 Published:2022-12-30
About author:WANG Pengfei (wangpf@dlut.edu.cn) is currently an associate professor with the School of Computer Science and Technology, Dalian University of Technology, China. His current research interests include edge intelligent computing and federated learning.|SONG Wei is currently pursuing her master’s degree with the School of Computer Science and Technology, Dalian University of Technology, China. Her current research interests focus on federated learning.|SUN Geng is currently an associate professor with the School of Computer Science and Technology, Jilin University, China. His current research interests include group intelligence and collaborative communications.|WEI Zongzheng is currently pursuing his master’s degree with the School of Computer Science and Technology, Dalian University of Technology, China. His current research interests focus on federated learning.|ZHANG Qiang is currently a Changjiang Scholar Professor with the College of Computer Science and Technology, Dalian University of Technology, China. His research interests focus on artificial intelligence.
Supported by:
the National Key Research and Development Program of China(2021ZD0112400);the NSFC(62202080);the NSFC-Liaoning Province United Foundation(U1908214);the CCF-Tencent Open Fund(IAGR20210116);the Fundamental Research Funds for the Central Universities(DUT21TD107);DUT20RC(3)039, and the Liaoning Revitalization Talents Program(XLYC2008017)

Abstract

Abstract:

Federated learning (FL) is a distributed machine learning approach that could provide secure 6G communications to preserve user privacy. In 6G communications, unmanned aerial vehicles (UAVs) are widely used as FL parameter servers to collect and broadcast related parameters due to the advantages of easy deployment and high flexibility. However, the challenge of limited energy restricts the popularization of UAV-enabled FL applications. An air-ground integrated low-energy federated learning framework is proposed, which minimizes the overall energy consumption of application communication while maintaining the quality of the FL model. Specifically, a hierarchical FL framework is proposed, where base stations (BSs) aggregate model parameters updated from their surrounding users separately and send the aggregated model parameters to the server, thereby reducing the energy consumption of communication. In addition, we optimize the deployment of UAVs through a deep Q-network approach to minimize their energy consumption for transmission as well as movement, thus improving the energy efficiency of the air-ground integrated system. The evaluation results show that our proposed method can reduce the system energy consumption while maintaining the accuracy of the FL model.

Key words: federated learning, 6G communications, privacy preserving, secure communication

WANG Pengfei, SONG Wei, SUN Geng, WEI Zongzheng, ZHANG Qiang. Air-Ground Integrated Low-Energy Federated Learning for Secure 6G Communications[J]. ZTE Communications, 2022, 20(4): 32-40.

Figures/Tables 5

Figure 1 Overview of AGILFL’s framework

Figure 2 Performance of total energy consumption

Figure 3 Movement trajectory of UAV

Figure 4 Performance of reward

Figure 5 Performance of accuracy

References 27

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Air-Ground Integrated Low-Energy Federated Learning for Secure 6G Communications

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