Ph.D Student
E-mail: taehwan@unist.ac.kr
Brief Bio: I have been a Ph.D. student affiliated with the Artificial Intelligence Graduate School (AIGS) at UNIST since March 2022. I obtained an MS (2019) degree from the Department of Electronic and Electrical Engineering at UNIST and a BS (2017) from the Department of Electronic Engineering at HBNU.
Federated and Decentralized Learning
Information and Communication Theory
Ph.D in Artificial Intelligence Graduate School, Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea.
Advisor: Sung Whan Yoon (Lab: MIIT)
Mar. 2022 ~ Present
MS in Electrical Engineering at Ulsan National Institute of Science and Technology (UNIST), Ulsan, South Korea.
Advisor: Jinho Chung
Mar. 2017 ~ Aug. 2019
B.S. in Electronic Engineering, Hanbat National University, Daejeon, South Korea.
Advisor: Kyoungjae Lee (Lab: WiSL)
Mar. 2011 ~ Feb. 2017.
*Equal contribution.
Taehwan Lee*, Kyeongkook Seo*, Jaejun Yoo, Sung Whan Yoon, "Understanding Flatness in Generative Models: Its Role and Benefits," in International Conference on Computer Vision (ICCV), Honolulu, HI, 2025. [arXiv] [Project]
SeungBum Ha*, Taehwan Lee*, Jiyoun Lim, Sung Whan Yoon, "Benchmarking Federated Learning for Semantic Datasets: Federated Scene Graph Generation," Pattern Recognition Letters [arXiv] [GitHub]
Taehwan Lee, Sung Whan Yoon, "Rethinking the Flat Minima Searching in Federated Learning," in the 41st International Conference on Machine Learning (ICML), Vienna, Austria, 2024. [paper][GitHub]
Taehwan Lee*, Hee-Heon Jung*, and Jin-Ho Chung. "A new one-coincidence frequency-hopping sequence set of length p2-p." 2018 IEEE Information Theory Workshop (ITW). IEEE, 2018.
"Rethinking the Flat Minima Searching in Federated Learning" is awarded as the finalist of Qualcomm Innovation Fellowship Korea (QIFK) 2024 [Link].
Teaching Assistant: Electrical Engineering Programming
Course: EE233, 2025 spring @ UNIST.
Description: Basic programming tools for electrical engineering (C++).
Teaching Assistant: Electrical Engineering Programming
Course: EE233, 2024 spring @ UNIST.
Description: Basic programming tools for electrical engineering (C++).
ETRI 위탁과제(다계층 연합학습 시뮬레이션 및 기여도 측정 도구 제작, Jun. 2024 - Nov. 2024)
Funded by ETRI
This research project is to develop a robust and flexible simulation environment for hierarchical and asynchronous federated learning algorithms.
My research is focused on coding the hierarchical and asynchronous structure of federated learning.
한국-미국(NSF) 국립과학재단 국제 공동 연구(Dec. 2021 - Nov. 2024)
Funded by IITP
My research part is focused on developing an algorithm that is robust to the heterogeneity of clients in federated learning.
Academic results: "Rethinking the Flat Minima Searching in Federated Learning" in the 41st ICML.
ETRI 위탁과제(다중 추론 성능 기반 연합학습 비동기 합의 실험 환경 구축, Jun. 2023 - Nov. 2023)
Funded by ETRI
This research project aims to develop a hierarchical and asynchronous federated learning algorithm.
ETRI 위탁과제(자원 은닉형 딥러닝 모델 실현 가능성 검증 기술 개발, Apr. 2023 - Nov. 2023)
Funded by ETRI
This research project aims to develop double-blind federated learning algorithms that secure both models and data.
My research is focused on adopting federated learning to Scene Graph Generation (SGG) tasks and constructing and analyzing the heterogeneity of the semantic dataset of Panoptic Scene Graph Generation (PSG) tasks.