| 研究課題/領域番号 |
23KJ1005
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| 研究種目 |
特別研究員奨励費
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| 配分区分 | 基金 |
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| 応募区分 | 国内 |
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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 | 国立情報学研究所 |
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研究代表者 |
CHEN CAIJUAN 国立情報学研究所, アーキテクチャ科学研究系, 特別研究員(PD)
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| 研究期間 (年度) |
2023-04-25 – 2025-03-31
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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 |
1,800千円 (直接経費: 1,800千円)
2024年度: 900千円 (直接経費: 900千円)
2023年度: 900千円 (直接経費: 900千円)
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| キーワード | Convergence analysis / Client selection / Energy management |
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| 研究開始時の研究の概要 |
To deal with weak channels and energy constraints for federated learning, a novel over-the-air federated learning system is proposed by optimizing device scheduling and energy management, in which, the impacts of intelligent reconfigurable surface settings and energy harvesting are investigated.
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| 研究実績の概要 |
This fiscal year's research concentrated on scheduling devices for over-the-air federated learning to improve the convergence speed of the system, addressing the challenges posed by weak channels and limited energy resources. The study involved a comprehensive literature review, model development, algorithm design, and simulation experiments. The findings demonstrate that optimizing the system model’s convergence analysis, device scheduling, and energy management, considering factors like transmission power, wireless channels, and energy constraints, can significantly enhance the system performance for over-the-air federated learning.
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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
Due to the availability of equipment in the laboratory for conducting simulations, such as GPUs and CPUs, and with the effective guidance of my supervisor, combined with regular and productive communication with both the supervisor and other collaborators, the overall research progress is proceeding as planned.
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| 今後の研究の推進方策 |
For the future research plan, we plan to optimize two-tier device scheduling under weak channel conditions based on previous studies for over-the-air federated learning. Furthermore, we will explore the configuration and optimization of reconfigurable intelligent surfaces to enhance the system performance of over-the-air federated learning with weak channels. In our prior research, the primary challenges involved establishing robust optimization models and developing effective optimization methods. To address these challenges and ensure the smooth progress of our future work, we will need to delve into additional relevant literature and acquire advanced optimization techniques.
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