| 研究課題/領域番号 |
22K17877
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| 研究種目 |
若手研究
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| 配分区分 | 基金 |
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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 | 東北大学 |
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研究代表者 |
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| 研究期間 (年度) |
2022-04-01 – 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 |
3,770千円 (直接経費: 2,900千円、間接経費: 870千円)
2025年度: 780千円 (直接経費: 600千円、間接経費: 180千円)
2024年度: 650千円 (直接経費: 500千円、間接経費: 150千円)
2023年度: 1,300千円 (直接経費: 1,000千円、間接経費: 300千円)
2022年度: 1,040千円 (直接経費: 800千円、間接経費: 240千円)
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| キーワード | Distributed Learning / Machine Learning / Computation Offloading / Satellite Networks / UAV Networks / Quantum Learning / 6G Networks / Network Management / Cloud Systems / Digital Twin / Federated Learning / Computer Networks |
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| 研究開始時の研究の概要 |
With more overlap between different cells in 6G, we need to carefully and smartly configure networks (deciding which frequency and access point to use). In this project, the applicant will use Federated Learning to minimize service delay and energy consumption, optimizing the performance.
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| 研究実績の概要 |
This year, 5 papers were published, including two international conference papers. Progress was made in multiple directions. First, an important paper was published that explains how Digital Twins can be used for multiple applications, including the distributed training of learning models. Two papers were published explaining how deployed servers in satellites and aerial vehicles can help with distributed processing and distributed learning. One paper was published explaining how changes in the environment can impact the learning models. Finally, the use of quantum learning in distributed drone systems was also evaluated.
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| 現在までの達成度 (区分) |
現在までの達成度 (区分)
1: 当初の計画以上に進展している
理由
Work has progressed well in training Machine Learning and in using Distributed Learning. I also recruited two international exchange students that have been phenomenal in helping out with the results of this project.
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| 今後の研究の推進方策 |
For the future, the implementation and use of Federated Learning models, especially in satellite environments, is scheduled.
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