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| Title | Prototype-Based Collaborative Learning In Uav-Assisted Edge Computing Networks |
|---|---|
| ID_Doc | 43587 |
| Authors | Yu E.; Dai H.; Zhang H.; Zheng Z.; Zhao J.; Chen G. |
| Year | 2025 |
| Published | Software - Practice and Experience, 55, 5 |
| DOI | http://dx.doi.org/10.1002/spe.3399 |
| Abstract | Context: The rise of artificial intelligence of things (AloT) has enabled smart cities and industries, and UAV-assisted edge computing networks are an important technology to support the above scenarios. UAV-assisted refers to leveraging UAVs as a dynamic, flexible infrastructure to assist edge network data processing and communication tasks. Multiple UAVs can use their own resources, and collaborate edge servers to train artificial intelligence (Al) models. Objective: Compared with cloud-based collaborative computing scenarios, UAV-assisted edge collaborative learning can reduce training and inference delays and improve user satisfaction. However, UAV-assisted edge networks scenario brings new challenges in terms of transmission burden and energy consumption. Method: This paper proposes a prototype-based joint optimization and training software system. The system consists of an optimization module and a training module. The optimization module first models an optimization problem including energy consumption and prototype error. Then it solves the optimization problem by problem transformation and plans the location of each UAV given the objects' position. After UAVs fly to the designated area and complete data collection, UAVs and the edge server train a model according to the proposed prototype-based collaborative training module. Our training module enables multiple UAVs and an edge server to collaboratively train a model by lightweight prototype transmission and prototype aggregation. We also prove the convergence of the proposed collaborative training method. Results: Results show our method reduces prototype error and energy consumption by at least 12.31% and improves model accuracy by 3.62% with a little communication burden. Conclusion: Finally, we verify system performance through experiments. © 2024 John Wiley & Sons Ltd. |
| Author Keywords | collaborative learning; prototype; UAV-assisted edge computing networks |
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