2023 Fiscal Year Annual Research Report
Federated Learning Infrastructure for Collaborative Machine Learning on Heterogeneous Environments
| Project/Area Number |
22KJ2289
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| Allocation Type | Multi-year Fund |
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| Research Institution | Osaka University |
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Principal Investigator |
Thonglek Kundjanasith 大阪大学, サイバーメディアセンター, 特任助教(常勤)
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| Project Period (FY) |
2023-03-08 – 2024-03-31
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| Keywords | Distributed storage / Dependable computing / Edge machine learning / Privacy Preservation / Resource Heterogeneity / Swarm learning / Tactile Internet / Ultra-distributed system |
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| Outline of Annual Research Achievements |
I have already finished developing my proposed infrastructure, LibearatAI. LibearatAI utilizes federated learning to train models while preserving data privacy. It enables individuals to collaboratively train models in their diverse environments, which typically vary. LibearatAI comprises three modules to support model training across different storage, computing, and communication resources: Furthermore, LibearatAI has been successfully applied to various applications, including hierarchical federated learning for predicting water levels in Thailand and decentralized federated learning for identifying plant diseases. These outcomes demonstrate LibearatAI's capacity to address challenges related to data sharing for data-driven services and extend its utility across diverse applications. In the future, LibearatAI is poised to become essential for ultra-distributed systems in the post-5G era. As the number of clients and data exponentially increases, its adaptability and privacy-preserving features will be crucial. LibearatAI's capacity to facilitate collaborative model training across diverse environments makes it a key solution for the challenges posed by escalating data demands.
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