Jun Xia is currently at the Hong Kong University of Science and Technology and the ACCESS Chip Center as a Postdoctoral Research Fellow, advised by Prof. Yuan Xie. He has been a Postdoctoral Research Associate at the University of Notre Dame since 2023 (advised by Prof. Yiyu Shi). He obtained his Ph.D. degree from East China Normal University in 2023 (advised by Prof. Mingsong Chen). He obtained his M.S. degree and B.S. degree from Jiangnan University (supervised by Prof. Zhilei Chai and Prof. Wei Yan from Peking University) and Hainan University in 2019 and 2016, respectively.
His research interests include Heterogeneous On-device Federated Learning and Trustworthy AI.
๐ Selected Publications
๐ฅ News
- 2026.02 ๐๐ A paper accepted by DAC 2026.
- 2026.02 ๐๐ Two papers accepted by CVPR 2026 (congratulations Boyu and Zixuan).
- 2024.12 ๐๐ A paper accepted by T-SUSC 2024.
- 2024.12 ๐๐ A paper accepted by DATE 2025.
- 2024.09 ๐๐ A paper accepted by NeurIPS 2024.
- 2024.07 ๐๐ A paper accepted by ICCAD 2024.
- 2024.02 ๐๐ A paper accepted by DAC 2024.
๐ Selected Publications
Jun Xia, Junqi Zhang, Zhaorong Zhu, Wenjie Chen, Mingsong Chen.
DAC 2026
CCF-A
Acceptance Ratio: 25%
Embedded System
We introduce RTFL, a novel energy-aware Real-Time Federated Learning framework based on Multi-Agent Reinforcement Learning (MARL), aiming to enhance the knowledge sharing across AIoT devices within a specified training time constraint. Specifically, RTFL employs an Adaptive Quantization-based Multi-Agent Scheduling (AQMAS) strategy, enabling a team of agents to intelligently select devices with specific model quantization levels for each round of local training, taking into account the resource constraints of the current devices.
Boyu Wang*, Jun Xia*, Mingsong Chen.
CVPR 2026
CCF-A
Acceptance Ratio: 25%
Computer Vision
Jun Xia, Zhihao Yue, Yingbo Zhou, Zhiwei Ling, Yiyu Shi, Xian Wei, Mingsong Chen.
NeurIPS 2024
CCF-A
Acceptance Ratio: 25%
Machine Learning
We obtain image high-frequency features through the Discrete Wavelet Transform (DWT) to generate backdoor triggers.
Jun Xia, Yi Zhang, Yiyu Shi.
ICCAD 2024
CCF-B
Acceptance Ratio: 24%
Embedded System / EDA
We propose an energy-aware FL framework named DR-FL, which considers the energy constraints in both clients and heterogeneous deep learning models to enable energy-efficient FL. Unlike Vanilla FL, DR-FL adopts our proposed Multi-Agent Reinforcement Learning (MARL)-based dual-selection method...
Ruiyang Qin, Jun Xia, Zhenge Jia, Meng Jiang, Ahmed Abbasi, Peipei Zhou, Jingtong Hu, Yiyu Shi.
DAC 2024
CCF-A
Acceptance Ratio: 25%
Embedded System
In this paper, we propose a novel framework to select and store the most representative data online in a self-supervised way. Such data has a small memory footprint and allows infrequent requests of user annotations for further fine-tuning.
Jun Xia, Ting Wang, Jiepin Ding, Xian Wei, Mingsong Chen.
IJCAI 2022
CCF-A
Acceptance Ratio: 14%
Artificial Intelligence
We introduce a novel backdoor defense framework named Attention Relation Graph Distillation (ARGD), which fully explores the correlation among attention features with different orders using our proposed Attention Relation Graphs (ARGs).
Jun Xia, Ming Hu, Xin Chen, Mingsong Chen.
DAC 2022
CCF-A
Acceptance Ratio: 23%
Embedded System
We fully exploit the parallel processing capability of the underlying hardware to enable a quick search for a barrier certificate.
Jun Xia, Tian Liu, Zhiwei Ling, Ting Wang, Xin Fu, Mingsong Chen.
TCAD 2022
CCF-A
Acceptance Ratio: 23%
Embedded System Journal
We propose a novel framework named PervasiveFL that enables efficient and effective FL among heterogeneous IoT devices. Without modifying original local models, PervasiveFL installs one lightweight NN model named modellet on each device.
Zhihao Yue, Jun Xia, Zhiwei Ling, Ming Hu, Ting Wang, Xian Wei, Mingsong Chen.
ACM MM 2022
CCF-A
Acceptance Ratio: 25%
Multi-Media
In this paper, we propose a novel two-stage backdoor defense method, named MCLDef, based on Model-Contrastive Learning (MCL). MCLDef can purify the backdoored model by pulling the feature representations of poisoned data towards those of their clean data counterparts.
๐ Educations
- 2023.09 - 2026.03 Postdoc, University of Notre Dame, Department of CSE. Advisor: Prof. Yiyu Shi.
- 2019.09 - 2023.06 PhD, East China Normal University, Department of Software Engineering (A-level subject rating). Supervisor: Prof. Mingsong Chen.
- 2016.09 - 2019.06 Master, Jiangnan University, Department of Internet of Things, Computer Science. Supervisor: Prof. Zhilei Chai.
๐ Honors and Awards
- 2024.12 The National Artificial Intelligence Research Resource (NAIRR) Pilot Award (PI; First Year; Cash Equivalent $98,400; Hope to see you in Washington, 2025.2.19 - 2.21).
- 2023.03 China National Scholarship ($4280)
- 2023.06 Shanghai Outstanding Graduates
- 2022.06 PhD Outstanding Program (ECNU) PI ($4280)
- 2021.06 Outstanding Student (ECNU)
๐ป Research Internships
- 2016.12 - 2019.06 Peking University, Supervisor: Prof. Wei Yan.
๐ฌ Invited Talks & Services
- 2026.03 Invited Talk at Shandong University.
- 2024.10 ICCAD 2024 Session Chair.
- 2022.12 Invited Talk at East China Normal University.
๐ Teachings
- 2025 Spring Official Instructor (Experimental Part), CSE 60685, University of Notre Dame.
- 2024 Spring Teaching Assistant, CSE 60685, University of Notre Dame.
His research interests include Heterogeneous On-device Federated Learning and Trustworthy AI.
๐ Reviews and PC Members
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