Energy-Efficient Federated Learning Through UAV Edge Under Location Uncertainties
Wang C. Tang X. Zhai D. Zhang R. Ussipov N. Zhang Y.
2025IEEE Computer Society
IEEE Transactions on Network Science and Engineering
2025#12Issue 1223 - 236 pp.
Federated Learning (FL) and Mobile Edge Computing (MEC) technologies alleviate the burden of deploying artificial intelligence (AI) on wireless devices with low computational capabilities. However, they also introduce energy consumption challenges in FL model training and data processing. In this paper, we employ Unmanned Aerial Vehicles (UAVs) to collect data from wireless devices and carry edge servers to assist the central server located at the base station in training FL model. We also consider the deviation of UAVs locations to address its impact on network performance. Specifically, we formulate a robust joint optimization problem to minimize the energy consumption of UAVs, considering the computational resources, transmit power, transmission time, and FL model accuracy. Moreover, Gaussian-distributed uncertainties caused by deviation in UAV locations result in probabilistic constraints on data offloading. We initially employ the Bernstein-type inequality (BTI) to transform probabilistic constraints into deterministic forms. Subsequently, we adopt the Block Coordinate Descent (BCD) to separate the problem into three subproblems. Simulation results demonstrate a significant reduction in energy consumption and superiority in robustness.
energy consumption , federated learning , Mobile edge computing , robust optimization
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School of Electronics and Information, Northwestern Polytechnical University, Xian, 710072, China
School of Information and Communication Engineering, Xian Jiaotong University, Xian, 710049, China
Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, 518063, China
National Key Laboratory of Wireless Communications, Chengdu, 611731, China
State Key Laboratory of Intelligent Game, Taicang, 215400, China
National Mobile Communications Research Laboratory, Southeast University, Nanjing, 211189, China
Department of Physics and Technology, Al-Farabi Kazakh National University, Almaty, 050040, Kazakhstan
Department of Informatics, University of Oslo, Oslo, 0316, Norway
School of Electronics and Information
School of Information and Communication Engineering
Research & Development Institute of Northwestern Polytechnical University in Shenzhen
National Key Laboratory of Wireless Communications
State Key Laboratory of Intelligent Game
National Mobile Communications Research Laboratory
Department of Physics and Technology
Department of Informatics
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