2023 Fiscal Year Research-status Report
A Study on Federated Learning for Efficient Communication Resource Allocation in 6G Heterogeneous Networks
| Project/Area Number |
22K17877
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| Research Institution | Tohoku University |
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Principal Investigator |
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| Project Period (FY) |
2022-04-01 – 2026-03-31
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| Keywords | Distributed Learning / Machine Learning / Computation Offloading / Satellite Networks / UAV Networks / Quantum Learning |
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| Outline of Annual Research Achievements |
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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| Current Status of Research Progress |
Current Status of Research Progress
1: Research has progressed more than it was originally planned.
Reason
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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| Strategy for Future Research Activity |
For the future, the implementation and use of Federated Learning models, especially in satellite environments, is scheduled.
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| Causes of Carryover |
The materials bought were slightly cheaper than anticipated, so there is a bit of leftover funds.
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Research Products
(5 results)
