[36] Differentially-Private Multi-Tier Federated Learning: A Formal Analysis and Evaluation
Frank Po-Chen Lin, Evan Chen, Dong-Jun Han, and Christopher G. Brinton
IEEE/ACM Transactions on Networking (ToN), 2026.
[35] ProLoG: Hybrid Prompt and LoRA Based Adaptation of Vision-Language Models for OOD Generalization
Jungwuk Park, Dong-Jun Han*, and Jaekyun Moon
AAAI Conference on Artificial Intelligence (AAAI), 2026.
[34] Local-Cloud Inference Offloading for LLMs in Multi-Modal, Multi-Task, Multi-Dialogue Settings [paper]
Liangqi Yuan, Dong-Jun Han, Shiqiang Wang, and Christopher G. Brinton
International Symposium on Theory, Algorithmic Foundations, Protocol Design for Mobile Networks and Mobile Computing (MobiHoc), 2025. (Best Paper Runner-up)
[33] LLMAP: LLM-Assisted Multi-Objective Route Planning with User Preferences
Liangqi Yuan, Dong-Jun Han, Christopher G. Brinton, and Sabine Brunswicker
Conference on Empirical Methods in Natural Language Processing (EMNLP Findings), 2025.
[32] Federated Learning over Hierarchical Wireless Networks: Training Latency Minimization via Submodel Partitioning [paper]
Wenzhi Fang, Dong-Jun Han, and Christopher G. Brinton
IEEE/ACM Transactions on Networking (ToN), Aug. 2025.
[31] Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translation [paper]
Yun-Wei Chu, Dong-Jun Han, and Christopher G. Brinton
IEEE Transactions on Audio, Speech and Language Processing (TASLP), May 2025.
[30] Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees [paper]
Shahryar Zehtabi, Dong-Jun Han, Rohit Parasnis, Seyyedali Hosseinalipour, and Christopher G. Brinton
International Conference on Learning Representations (ICLR), Apr. 2025. (Spotlight Paper)
[29] Asynchronous Federated Reinforcement Learning with Policy Gradient Updates: Algorithm Design and Convergence Analysis [paper]
Guangchen Lan, Dong-Jun Han, Abolfazl Hashemi, Vaneet Aggarwal, and Christopher G. Brinton
International Conference on Learning Representations (ICLR), Apr. 2025.
[28] Unlocking the Potential of Model Calibration in Federated Learning [paper]
Yun-Wei Chu, Dong-Jun Han, Seyyedali Hosseinalipour, and Christopher G. Brinton
International Conference on Learning Representations (ICLR), Apr. 2025.
[27] PRISM: Privacy-Preserving Improved Stochastic Masking for Federated Generative Models [paper]
Kyeongkook Seo, Dong-Jun Han*, and Jaejun Yoo*
International Conference on Learning Representations (ICLR), Apr. 2025.
[26] Adaptive Energy Alignment for Accelerating Test-Time Adaptation [paper]
Wonjeong Choi, Do-Yeon Kim, Jungwuk Park, Jungmoon Lee, Younghyun Park, Dong-Jun Han*, and Jaekyun Moon
International Conference on Learning Representations (ICLR), Apr. 2025.
[25] Rethinking the Starting Point: Collaborative Pre-Training for Federated Downstream Tasks [paper]
Yun-Wei Chu, Dong-Jun Han, Seyyedali Hosseinalipour, and Christopher G. Brinton
AAAI Conference on Artificial Intelligence (AAAI), Feb. 2025.
[24] Hierarchical Federated Learning with Multi-Timescale Gradient Correction [paper]
Wenzhi Fang, Dong-Jun Han, Evan Chen, Shiqiang Wang, and Christopher G. Brinton
Neural Information Processing Systems (NeurIPS), Dec. 2024.
[23] Orchestrating Federated Learning in Space-Air-Ground Integrated Networks: Adaptive Data Offloading and Seamless Handover [paper]
Dong-Jun Han, Wenzhi Fang, Seyyedali Hosseinalipour, Mung Chiang, and Christopher G. Brinton
IEEE Journal on Selected Areas in Communications (JSAC), Dec. 2024.
[22] Cooperative Federated Learning over Ground-to-Satellite Integrated Networks: Joint Local Computation and Data Offloading [paper]
Dong-Jun Han, Seyyedali Hosseinalipour, David J. Love, Mung Chiang, and Christopher G. Brinton
IEEE Journal on Selected Areas in Communications (JSAC), May 2024.
[21] Achieving Lossless Gradient Sparsification via Mapping to Alternative Space in Federated Learning [paper]
Do-Yeon Kim, Dong-Jun Han*, Jun Seo, and Jaekyun Moon
International Conference on Machine Learning (ICML), July 2024.
[20] Federated Split Learning with Joint Personalization-Generalization for Inference-Stage Optimization in Wireless Edge Networks [paper]
Dong-Jun Han, Do-Yeon Kim, Minseok Choi, David Nickel, Jaekyun Moon, Mung Chiang, and Christopher G. Brinton
IEEE Transactions on Mobile Computing (TMC), June 2024.
[19] Consistency-Guided Temperature Scaling using Style and Content Information for Out-of-Domain Calibration [paper]
Wonjeong Choi, Jungwuk Park, Dong-Jun Han*, Younghyun Park, and Jaekyun Moon
AAAI Conference on Artificial Intelligence (AAAI), Feb. 2024.
[18] StableFDG: Style and Attention Based Learning for Federated Domain Generalization [paper]
Jungwuk Park*, Dong-Jun Han*, Jinho Kim, Shiqiang Wang, Christopher G. Brinton, and Jaekyun Moon
Neural Information Processing Systems (NeurIPS), Dec. 2023.
[17] NEO-KD: Knowledge-Distillation-Based Adversarial Training for Robust Multi-Exit Neural Networks [paper]
Seokil Ham, Jungwuk Park, Dong-Jun Han*, and Jaekyun Moon
Neural Information Processing Systems (NeurIPS), Dec. 2023.
[16] Test-Time Style Shifting: Handling Arbitrary Styles in Domain Generalization [paper]
Jungwuk Park*, Dong-Jun Han*, Soyeong Kim, and Jaekyun Moon
International Conference on Machine Learning (ICML), July 2023.
[15] SplitGP: Achieving Both Generalization and Personalization in Federated Learning [paper]
Dong-Jun Han, Do-Yeon Kim, Minseok Choi, Christopher G. Brinton, and Jaekyun Moon
IEEE International Conference on Computer Communications (INFOCOM), May 2023 (Acceptance rate: 19.2%).
[14] Improving Low-Latency Predictions in Multi-Exit Neural Networks via Block-Dependent Losses [paper]
Dong-Jun Han*, Jungwuk Park*, Seokil Ham, Namjin Lee, and Jaekyun Moon
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), accepted, 2023.
[13] Warping the Space: Weight Space Rotation for Class-Incremental Few-Shot Learning [paper]
Do-Yeon Kim, Dong-Jun Han*, Jun Seo, and Jaekyun Moon
International Conference on Learning Representations (ICLR), May 2023. (Spotlight presentation: notable-top-25%)
[12] Active Learning for Object Detection with Evidential Deep Learning and Hierarchical Uncertainty Aggregation [paper]
Younghyun Park, Wonjeong Choi, Soyeong Kim, Dong-Jun Han*, and Jaekyun Moon
International Conference on Learning Representations (ICLR), May 2023.
[11] FedMes: Speeding Up Federated Learning with Multiple Edge Servers [paper]
Dong-Jun Han, Minseok Choi, Jungwuk Park, and Jaekyun Moon
IEEE Journal on Selected Areas in Communications (JSAC), Dec. 2021.
[10] Coded Wireless Distributed Computing with Packet Losses and Retransmissions [paper]
Dong-Jun Han, Jy-yong Sohn, and Jaekyun Moon
IEEE Transactions on Wireless Communications (TWC), Dec. 2021.
[9] Few-Round Learning for Federated Learning [paper]
Younghyun Park*, Dong-Jun Han*, Do-Yeon Kim, Jun Seo, and Jaekyun Moon
Neural Information Processing Systems (NeurIPS), Dec. 2021.
[8] Sageflow: Robust Federated Learning against Both Stragglers and Adversaries [paper]
Jungwuk Park*, Dong-Jun Han*, Minseok Choi, and Jaekyun Moon
Neural Information Processing Systems (NeurIPS), Dec. 2021.
[7] TiBroco: A Fast and Secure Distributed Learning Framework for Tiered Wireless Edge Networks [paper]
Dong-Jun Han, Jy-yong Sohn, and Jaekyun Moon
IEEE International Conference on Computer Communications (INFOCOM), May 2021. (Acceptance rate: 19.9%)
[6] Hierarchical Broadcast Coding: Expediting Distributed Learning at the Wireless Edge [paper]
Dong-Jun Han, Jy-yong Sohn, and Jaekyun Moon
IEEE Transactions on Wireless Communications (TWC), Apr. 2021. (Qualcomm-KAIST Innovation Award)
[5] Probabilistic Caching and Dynamic Delivery Policies for Categorized Contents and Consecutive User Demands [paper]
Minseok Choi, Andreas F. Molisch, Dong-Jun Han, Dongjae Kim, Joongheon Kim, and Jaekyun Moon
IEEE Transactions on Wireless Communications (TWC), Apr. 2021.
[4] Election Coding for Distributed Learning: Protecting SignSGD against Byzantine Attacks [paper]
Jy-yong Sohn, Dong-Jun Han, Beongjun Choi, and Jaekyun Moon
Neural Information Processing Systems (NeurIPS), Dec. 2020.
[3] Bi-Directional Cooperative NOMA Without Full CSIT [paper]
Minseok Choi, Dong-Jun Han, and Jaekyun Moon
IEEE Transactions on Wireless Communications (TWC), Nov. 2018.
[2] Combined Window-Filter Waveform Design With Transmitter-Side Channel State Information [paper]
Dong-Jun Han, Jaekyun Moon, Jy-yong Sohn, Sunyoung Jo, and Jang Hun Kim
IEEE Transactions on Vehicular Technology (TVT), Sep. 2018.
[1] Combined Subband-Subcarrier Spectral Shaping in Multi-Carrier Modulation under the Excess Frame Length Constraint [paper]
Dong-Jun Han, Jaekyun Moon, Dongjae Kim, Sae-Young Chung, and Yong H. Lee
IEEE Journal on Selected Areas in Communications (JSAC), June 2017.
[16] Differentially-Private Multi-Tier Federated Learning [paper]
Evan Chen*, Frank Po-Chen Lin*, Dong-Jun Han, and Christopher G. Brinton
IEEE International Conference on Communications (ICC), June 2025.
[15] FICDF: A Federated Incremental Learning Framework for IoT Device Fingerprinting [paper]
Shengli Ding, Dong-Jun Han, Christopher G. Brinton, and Keerthi Dasala
International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks (WiOpt), Oct. 2024.
[14] Cooperative Federated Learning over Hybrid Terrestrial and Non-Terrestrial Networks [paper]
Dong-Jun Han, Seyyedali Hosseinalipour, David J. Love, Mung Chiang, and Christopher G. Brinton
IEEE International Conference on Communications (ICC), June 2024.
[13] Submodel Partitioning in Hierarchical Federated Learning: Algorithm Design and Convergence Analysis [paper]
Wenzhi Fang, Dong-Jun Han, and Christopher G. Brinton
IEEE International Conference on Communications (ICC), June 2024.
[12] FedMFS: Federated Multimodal Fusion Learning with Selective Modality Communication [paper]
Liangqi Yuan, Dong-Jun Han, Vishnu Pandi Chellapandi, Stanislaw H Żak, and Christopher G. Brinton
IEEE International Conference on Communications (ICC), June 2024.
[11] Only Send What You Need: Learning to Communicate Efficiently in Federated Multilingual Machine Translation
Yun-Wei Chu, Dong-Jun Han, and Christopher G. Brinton
The Web Conference Workshop on Federated Foundation Models for the Web (WWW Workshop), May 2024.
[10] Distribution Aware Active Learning via Gaussian Mixtures [paper]
Younghyun Park, Dong-Jun Han, Jungwuk Park, Wonjeong Choi, Humaira Kousar, and Jaekyun Moon
ICLR Workshop on Pitfalls of Limited Data and Computation for Trustworthy ML (ICLR Workshop), May 2023.
[9] Style Balancing and Test-Time Style Shifting for Domain Generalization
Jungwuk Park*, Dong-Jun Han*, Soyeong Kim, and Jaekyun Moon
ICML Workshop on Principles of Distribution Shift (ICML Workshop), July 2022.
[8] Training Multi-Exit Architectures via Block-Dependent Losses for Anytime Inference [paper]
Dong-Jun Han*, Jungwuk Park*, Seokil Ham, Namjin Lee ,and Jaekyun Moon
CVPR Workshop on Dynamic Neural Networks Meet Computer Vision (CVPR Workshop), June 2022 (Oral).
[7] Active Object Detection with Epistemic Uncertainty and Hierarchical Information Aggregation [paper]
Younghyun Park, Soyeong Kim, Wonjeong Choi, Dong-Jun Han, and Jaekyun Moon
CVPR Workshop on Efficient Deep Neural Network for Computer Vision (CVPR Workshop), June 2022.
[6] Accelerating Federated Learning with Split Learning on Locally Generated Losses [paper]
Dong-Jun Han, Hasnain Irshad Bhatti, Jungmoon Lee, and Jaekyun Moon
ICML Workshop on Federated Learning for User Privacy and Data Confidentiality (ICML Workshop), July 2021.
[5] Handling Both Stragglers and Adversaries for Robust Federated Learning [paper]
Jungwuk Park*, Dong-Jun Han*, Minseok Choi, and Jaekyun Moon
ICML Workshop on Federated Learning for User Privacy and Data Confidentiality (ICML Workshop), July 2021.
[4] Cache Allocations for Consecutive Requests of Categorized Contents: Service Provider’s Perspective [paper]
Minseok Choi, Andreas Molisch, Dong-Jun Han, Joongheon Kim, and Jaekyun Moon
IEEE Wireless Communications and Networking Conference (WCNC), Apr. 2020.
[3] Coded Distributed Computing over Packet Erasure Channels [paper]
Dong-Jun Han, Jy-yong Sohn, and Jaekyun Moon
IEEE International Symposium on Information Theory (ISIT), July 2019.
[2] Scalable Network-Coded PBFT Consensus Algorithm [paper]
Beongjun Choi, Jy-yong Sohn, Dong-Jun Han, and Jaekyun Moon
IEEE International Symposium on Information Theory (ISIT), July 2019.
[1] Probabilistic Caching Policy for Categorized Contents and Consecutive User Demands [paper]
Minseok Choi, Dongjae Kim, Dong-Jun Han, Joongheon Kim, and Jaekyun Moon
IEEE International Conference on Communications (ICC), May 2019.