Sageflow: Robust Federated Learning against Both Stragglers and Adversaries. - Jungwuk Park; Dong-Jun Han; Minseok Choi; Jaekyun Moon (NeurIPS, 2021)

FedMes: Speeding Up Federated Learning With Multiple Edge Servers. - Dong-Jun Han; Minseok Choi; Jungwuk Park; Jaekyun Moon (IEEE J. Sel. Areas Commun., 2021)

Test-Time Style Shifting: Handling Arbitrary Styles in Domain Generalization. - Jungwuk Park; Dong-Jun Han; Soyeong Kim; Jaekyun Moon (ICML, 2023)

NEO-KD: Knowledge-Distillation-Based Adversarial Training for Robust Multi-Exit Neural Networks. - Seokil Ham; Jungwuk Park; Dong-Jun Han; Jaekyun Moon (NeurIPS, 2023)

StableFDG: Style and Attention Based Learning for Federated Domain Generalization. - Jungwuk Park; Dong-Jun Han; Jinho Kim; Shiqiang Wang 001; Christopher G. Brinton; Jaekyun Moon (NeurIPS, 2023)

Consistency-Guided Temperature Scaling Using Style and Content Information for Out-of-Domain Calibration. - Wonjeong Choi; Jungwuk Park; Dong-Jun Han; Younghyun Park; Jaekyun Moon (AAAI, 2024)

Improving Low-Latency Predictions in Multi-Exit Neural Networks via Block-Dependent Losses. - Dong-Jun Han; Jungwuk Park; Seokil Ham; Namjin Lee; Jaekyun Moon (IEEE Trans. Neural Networks Learn. Syst., 2024)

Adaptive Energy Alignment for Accelerating Test-Time Adaptation. - Wonjeong Choi; Do-Yeon Kim 001; Jungwuk Park; Jungmoon Lee; Younghyun Park; Dong-Jun Han; Jaekyun Moon (ICLR, 2025)