Federated Learning · Decentralized Learning · Reinforcement Learning
Chungbuk National University (CBNU) · South Korea
I work on federated and decentralized machine learning, with an interest in efficient communication and reinforcement learning. My research explores how distributed agents can learn collaboratively, with applications in intelligent transportation.
This profile brings together my research implementations, experiment resources, and learning projects.
- Federated learning: collaborative training across distributed data sources.
- Decentralized learning: communication-efficient model exchange and cooperation.
- Reinforcement learning: multi-agent decision-making and traffic simulation.
| Project | Focus | Resources |
|---|---|---|
| FLDQN | Federated reinforcement learning for agent cooperation and travel-time minimization using SUMO. | Paper · Code |
| AEDFL | Autoencoder-based decentralized federated learning for efficient communication. | Paper |
| From Centralized to Federated | Practical experiments exploring the transition from centralized training to federated learning. | Code |
Find my publications on Google Scholar and ResearchGate, or connect with me on LinkedIn.