| 研究実績の概要 |
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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