Trustworthy decentralized AI for large-scale IoT representation learning
| Project/Area Number |
22KJ0878
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| Project/Area Number (Other) |
22J12681 (2022)
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Multi-year Fund (2023)
Single-year Grants (2022) |
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| Section | 国内 |
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| Review Section |
Basic Section 61030:Intelligent informatics-related
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| Research Institution | The University of Tokyo |
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Principal Investigator |
SUN YUWEI 東京大学, 情報理工学系研究科, 特別研究員(DC2)
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| Project Period (FY) |
2023-03-08 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥1,700,000 (Direct Cost: ¥1,700,000)
Fiscal Year 2023: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2022: ¥900,000 (Direct Cost: ¥900,000)
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| Keywords | transfer learning / multi-modal / Transformer / modularity / attention / machine learning / neural networks / life-long learning / AI security / data privacy / decentralized ML / edge computing |
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| Outline of Research at the Start |
This project aims to develop a trustworthy decentralized AI framework for the life-long representation learning within multi-modal AI models. A feasible and scalable system hinges on overcoming key challenges of reusable knowledge transfer, communication efficiency, and adversarial robustness.
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| Outline of Annual Research Achievements |
This project delved into localized learning in multi-modal distributed machine learning to address the out-of-distribution problem. The study initially explored knowledge transfer among a group of expert models observing partial environments in a federated learning setup. It demonstrated that generalization could be attained through the coordination of localized models by extracting domain-invariant knowledge with a global model. An additional approach is the Markov chain-based Homogeneous Learning, where a meta-observer learns an efficient communication policy of individual models.
Overall, this project proposed novel approaches to reusable neural modules for distributed machine learning in real-world learning settings. The project contributed to invited talks and multiple publications in both journals and top conferences in the field.
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Report
(2 results)
Research Products
(25 results)
