政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/155067

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Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/155067

Title:	AL-powered EdgeFL: Achieving Low Latency and High Accuracy in Federated Learning
Authors:	張宏慶 Jang, Hung-Chin;Chang, Hao-Po
Contributors:	資訊系
Keywords:	Associated Learning;Federated Learning;Collaborative Machine Learning;Mobile Edge Computing;Device-to-Device Communication
Date:	2024-06
Issue Date:	2025-01-07 09:35:51 (UTC+8)
Abstract:	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.
Relation:	Proc. of the The 2024 IEEE 99th Vehicular Technology Conference, IEEE Vehicular Technology Society
Data Type:	conference
DOI link:	https://doi.org/10.1109/VTC2024-Spring62846.2024.10683166
DOI:	10.1109/VTC2024-Spring62846.2024.10683166
Appears in Collections:	[Department of Computer Science ] Proceedings

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