| Project/Area Number |
23KJ1005
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Multi-year Fund |
|---|
| Section | 国内 |
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| Review Section |
Basic Section 60060:Information network-related
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| Research Institution | National Institute of Informatics |
|---|
Principal Investigator |
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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|---|
| Project Period (FY) |
2023-04-25 – 2025-03-31
|
| Project Status |
Granted (Fiscal Year 2023)
|
| Budget Amount *help |
¥1,800,000 (Direct Cost: ¥1,800,000)
Fiscal Year 2024: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2023: ¥900,000 (Direct Cost: ¥900,000)
|
|---|
| Keywords | Convergence analysis / Client selection / Energy management |
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| Outline of Research at the Start |
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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| Outline of Annual Research Achievements |
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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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
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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| Strategy for Future Research Activity |
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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