Split Federated Learning for Low-Altitude Wireless Networks: Joint Sensing, Communication, Computation, and Control Co-design
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911407745794048 |
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| author | Hou, Xiangwang Wang, Xianghe Wang, Jiacheng Zhang, Zekai Du, Jun Wang, Jingjing Ren, Yong |
| author_facet | Hou, Xiangwang Wang, Xianghe Wang, Jiacheng Zhang, Zekai Du, Jun Wang, Jingjing Ren, Yong |
| contents | Unmanned aerial vehicles (UAVs) with integrated sensing, communication, computation and control (ISC3) capabilities have become key enablers of next-generation wireless networks. Federated edge learning (FEL) leverages UAVs as mobile learning agents to collect data, perform local model updates, and contribute to global model aggregation. However, existing UAV-assisted FEL systems face critical challenges, including excessive computational demands, privacy risks, and inefficient communication, primarily due to the requirement for full-model training on resource-constrained UAVs. To deal with aforementioned challenges, we propose Split Federated Learning for UAV-Enabled ISC3 (SFLSC3), a novel framework that integrates split federated learning (SFL) into UAV-assisted FEL. SFLSC3 optimally partitions model training between UAVs and edge servers, significantly reducing UAVs' computational burden while preserving data privacy. We conduct a theoretical analysis of UAV deployment, split point selection, data sensing volume, and client-side aggregation frequency, deriving closed-form upper bounds for the convergence gap. Based on these insights, we conceive a joint optimization problem to minimize the delay required to achieve a target model accuracy. Given the non-convex nature of the problem, we develop a low-complexity algorithm to efficiently determine UAV deployment, split point selection, and communication frequency. Extensive simulations on a target motion recognition task validate the effectiveness of SFLSC3, demonstrating superior convergence and delay performance compared to baseline methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_01443 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Split Federated Learning for Low-Altitude Wireless Networks: Joint Sensing, Communication, Computation, and Control Co-design Hou, Xiangwang Wang, Xianghe Wang, Jiacheng Zhang, Zekai Du, Jun Wang, Jingjing Ren, Yong Distributed, Parallel, and Cluster Computing Emerging Technologies Unmanned aerial vehicles (UAVs) with integrated sensing, communication, computation and control (ISC3) capabilities have become key enablers of next-generation wireless networks. Federated edge learning (FEL) leverages UAVs as mobile learning agents to collect data, perform local model updates, and contribute to global model aggregation. However, existing UAV-assisted FEL systems face critical challenges, including excessive computational demands, privacy risks, and inefficient communication, primarily due to the requirement for full-model training on resource-constrained UAVs. To deal with aforementioned challenges, we propose Split Federated Learning for UAV-Enabled ISC3 (SFLSC3), a novel framework that integrates split federated learning (SFL) into UAV-assisted FEL. SFLSC3 optimally partitions model training between UAVs and edge servers, significantly reducing UAVs' computational burden while preserving data privacy. We conduct a theoretical analysis of UAV deployment, split point selection, data sensing volume, and client-side aggregation frequency, deriving closed-form upper bounds for the convergence gap. Based on these insights, we conceive a joint optimization problem to minimize the delay required to achieve a target model accuracy. Given the non-convex nature of the problem, we develop a low-complexity algorithm to efficiently determine UAV deployment, split point selection, and communication frequency. Extensive simulations on a target motion recognition task validate the effectiveness of SFLSC3, demonstrating superior convergence and delay performance compared to baseline methods. |
| title | Split Federated Learning for Low-Altitude Wireless Networks: Joint Sensing, Communication, Computation, and Control Co-design |
| topic | Distributed, Parallel, and Cluster Computing Emerging Technologies |
| url | https://arxiv.org/abs/2504.01443 |