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題名 AL-powered EdgeFL: Achieving Low Latency and High Accuracy in Federated Learning
作者 張宏慶
Jang, Hung-Chin;Chang, Hao-Po
貢獻者 資訊系
關鍵詞 Associated Learning; Federated Learning; Collaborative Machine Learning; Mobile Edge Computing; Device-to-Device Communication
日期 2024-06
上傳時間 7-Jan-2025 09:35:51 (UTC+8)
摘要 Recent advancements in mobile networks, the proliferation of powerful edge devices, AI breakthroughs, and heightened data privacy concerns have spurred the adoption of distributed machine learning approaches like Federated Learning (FL) and Split Learning (SL), each with pros and cons. This paper introduces a novel training framework designed to match the accuracy of FL while minimizing edge device workload, edge server data traffic, and model usage latency to enhance user experience. The proposed architecture features a dual-layer setup, employs a heuristic clustering algorithm, and enables grouped edge devices to train segments of the model. This approach leverages device-to-device (D2D) communication and the Associated Learning (AL) model to address model partitioning. Furthermore, it streamlines communication by having only the primary device in each group liaise with the edge server, thereby alleviating server traffic. Through PyTorch and ns3 simulations, this study demonstrates its capability to improve accuracy, reduce latency, and enhance user experience, effectively lightening the load on edge devices and servers in specific scenarios.
關聯 Proc. of the The 2024 IEEE 99th Vehicular Technology Conference, IEEE Vehicular Technology Society
資料類型 conference
DOI https://doi.org/10.1109/VTC2024-Spring62846.2024.10683166
dc.contributor 資訊系
dc.creator (作者) 張宏慶
dc.creator (作者) Jang, Hung-Chin;Chang, Hao-Po
dc.date (日期) 2024-06
dc.date.accessioned 7-Jan-2025 09:35:51 (UTC+8)-
dc.date.available 7-Jan-2025 09:35:51 (UTC+8)-
dc.date.issued (上傳時間) 7-Jan-2025 09:35:51 (UTC+8)-
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/155067-
dc.description.abstract (摘要) Recent advancements in mobile networks, the proliferation of powerful edge devices, AI breakthroughs, and heightened data privacy concerns have spurred the adoption of distributed machine learning approaches like Federated Learning (FL) and Split Learning (SL), each with pros and cons. This paper introduces a novel training framework designed to match the accuracy of FL while minimizing edge device workload, edge server data traffic, and model usage latency to enhance user experience. The proposed architecture features a dual-layer setup, employs a heuristic clustering algorithm, and enables grouped edge devices to train segments of the model. This approach leverages device-to-device (D2D) communication and the Associated Learning (AL) model to address model partitioning. Furthermore, it streamlines communication by having only the primary device in each group liaise with the edge server, thereby alleviating server traffic. Through PyTorch and ns3 simulations, this study demonstrates its capability to improve accuracy, reduce latency, and enhance user experience, effectively lightening the load on edge devices and servers in specific scenarios.
dc.format.extent 121 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Proc. of the The 2024 IEEE 99th Vehicular Technology Conference, IEEE Vehicular Technology Society
dc.subject (關鍵詞) Associated Learning; Federated Learning; Collaborative Machine Learning; Mobile Edge Computing; Device-to-Device Communication
dc.title (題名) AL-powered EdgeFL: Achieving Low Latency and High Accuracy in Federated Learning
dc.type (資料類型) conference
dc.identifier.doi (DOI) 10.1109/VTC2024-Spring62846.2024.10683166
dc.doi.uri (DOI) https://doi.org/10.1109/VTC2024-Spring62846.2024.10683166