| 研究課題/領域番号 |
23K11080
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| 研究種目 |
基盤研究(C)
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| 配分区分 | 基金 |
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| 応募区分 | 一般 |
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| 審査区分 |
小区分60060:情報ネットワーク関連
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| 研究機関 | 茨城大学 |
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研究代表者 |
王 瀟岩 茨城大学, 理工学研究科(工学野), 准教授 (10725667)
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| 研究分担者 |
梅比良 正弘 南山大学, 理工学部, 教授 (00436239)
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| 研究期間 (年度) |
2023-04-01 – 2026-03-31
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| 研究課題ステータス |
交付 (2023年度)
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| 配分額 *注記 |
4,680千円 (直接経費: 3,600千円、間接経費: 1,080千円)
2025年度: 1,040千円 (直接経費: 800千円、間接経費: 240千円)
2024年度: 1,950千円 (直接経費: 1,500千円、間接経費: 450千円)
2023年度: 1,690千円 (直接経費: 1,300千円、間接経費: 390千円)
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| キーワード | split learning / in-network learning / resource constraint |
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| 研究開始時の研究の概要 |
The booming mobile deep learning applications that enable personalized experiences are challenging the current computing and communication network architectures. The state-of-the-art federated learning (FL) and split learning (SL) based solutions, however, are constrained by computational resources and communication resources respectively. To this end, in this research, we propose an efficient split in-network learning approach for resource constrained wireless networks, which amalgamates the FL and SL techniques and eliminates their inherent drawbacks.
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| 研究実績の概要 |
In FY2023, we have realized the efficient split in-network learning approach with fixed model and network settings. Specifically, we focused on the realization of BS-side model virtualization and UE-BS model’s gradient aggregation, under a given model partition and UE set. We validated the proposed approach’s convergence speed and model accuracy by simulations on PyTorch. Different from most of the previous studies that use MNIST dataset (i.e., handwritten digits from 0 to 9), we took into consideration the real mobile deep learning applications, and thus used an aerial view human action detection dataset for evaluation.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
We consider that the research progresses smoothly. We have published 1 journal papers, 2 internal conference papers and multiple domestic conference papers in FY2023 under the support of this funding.
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| 今後の研究の推進方策 |
In the following year, we will consider to refine the approach and clarify the performance tradeoff curve under various resource budget. Furthermore, we will consider to implement the proposed approach on testbed.
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