Yuben Qu
ABSTRACT
Traditional federated learning (FL) usually relies on static ground base stations (BSs) for model aggregation. Unmanned aerial vehicles (UAVs), due to their flexible 3D deployment, can effectively complement ground BSs, enabling the establishment of air-ground integrated FL to boost ubiquitous edge intelligence. However, for online FL under such an air-ground setting, resource-limited users need to determine the resource allocation and sample selection to enhance training performance and achieve high energy efficiency. This paper studies how to minimize the tradeoff between energy consumption and training performance of air-gound online FL, and proposes an efficient algorithm to solve the aforementioned problem. Simulation results validate the effectiveness of our proposed algorithm in terms of both training effect and energy consumption.
- Lingshuang Cai, Di Lin, Jiale Zhang, and Shui Yu. 2020. Dynamic Sample Selection for Federated Learning with Heterogeneous Data in Fog Computing. In IEEE ICC 2020. 1–6.Google Scholar
- Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar. 2020. Personalized Federated Learning: A Meta-Learning Approach. Available: https://doi.org/10.48550/arXiv.2002.07948 (2020).Google Scholar
- Yuqian Jing, Yuben Qu, Chao Dong, Weiqing Ren, Yun Shen, Qihui Wu, and Song Guo. 2023. Exploiting UAV for Air-Ground Integrated Federated Learning: A Joint UAV Location and Resource Optimization Approach. IEEE TGCN (2023), 1–1.Google Scholar
- Anran Li, Lan Zhang, Juntao Tan, Yaxuan Qin, Junhao Wang, and Xiang-Yang Li. 2021. Sample-level Data Selection for Federated Learning. In IEEE INFOCOM 2021. 1–10.Google Scholar
- Nguyen H. Tran, Wei Bao, Albert Zomaya, Minh N. H. Nguyen, and Choong Seon Hong. 2019. Federated Learning over Wireless Networks: Optimization Model Design and Analysis. In IEEE INFOCOM 2019. 1387–1395.Google ScholarDigital Library
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