Xiaoyong (Brian) Yuan, Ph.D.

I am an Assistant Professor in the Holcombe Department of Electrical and Computer Engineering at Clemson University. Before joining Clemson, I was an Assistant Professor in the College of Computing at Michigan Technological University. I received my Ph.D. in computer science from the University of Florida (2020), M.E. in computer engineering from Peking University (2015), and B.S. in Mathematics from Fudan University (2012). 

My research spans the fields of machine learning, security & privacy, and edge computing. I was the recipient of the ORAU Ralph E. Powe Junior Faculty Enhancement Award (2022), the Michigan Tech ICC Achievement Award (2022), and the IEEE CIS TETCI Outstanding Paper Award (2025). I have been serving as an associate editor for IEEE Transactions on Neural Networks and Learning Systems (TNNLS) since 2022 and a reviewer for top-tier conferences, e.g., NeurIPS, ICML, ICLR, and AAAI. My research is supported by NSF, NIH, and industry.

I am looking for self-motivated Ph.D. (RA support), master, and undergraduate students. If interested, please send your CV, transcripts (unofficial copies are acceptable), and English test scores to xiaoyon AT clemson DOT edu. 

If you want to serve as a reviewer for IEEE TNNLS, please contact me via xiaoyon AT clemson DOT edu. 

News

• 08/25: Our paper "Your RAG is Unfair: Exposing Fairness Vulnerabilities in Retrieval-Augmented Generation via Backdoor Attacks" has been accepted at EMNLP 2025 (main conference). Congratulations, Gaurav!

• 08/25: Our paper "AIP: Subverting Retrieval-Augmented Generation via Adversarial Instructional Prompt" has been accepted at EMNLP 2025  (main conference). Congratulations, Saket!

• 08/25: Our paper "What Lurks Within? Concept Auditing for Shared Diffusion Models at Scale" has been accepted at CCS 2025.

• 08/25: I have been invited to serve as a TPC member of Usenix Security 2026.

• 07/25: Congratulations to Saket, who has successfully defended his PhD dissertation!

• 03/25: Our paper "You Don't Need All Attentions: Distributed Dynamic Fine-Tuning for Foundation Models" has been accepted at IJCNN 2025. Congratulations, Shiwei!

• 02/25: Our paper "Is Embedding-as-a-Service Safe? Meta-Prompt-Based Backdoor Attacks for User-Specific Trigger Migration" has been accepted by Transactions on Artificial Intelligence. Congrats to Gaurav!

• 10/24: Our paper "A single-step, sharpness-aware minimization is all you need to achieve efficient and accurate sparse training" has been accepted at NeurIPS 2024.

• 08/24: I have been invited to serve as the session chair of Trustworthy AI in IJCAI, 2024.

• 07/24: Our paper "ES Attack: Model Stealing Against Deep Neural Networks Without Data Hurdles" has been selected to receive the 2025 IEEE CIS TETCI Outstanding Paper Award. Thanks, IEEE Computational Intelligence Society!

• 04/24: Our paper "BadFusion: 2D-Oriented Backdoor Attacks against 3D Object Detection" has been accepted by IJCAI 2024. Congratulations, Saket!

• 12/23: My lab will join the Holcombe Department of Electrical and Computer Engineering at Clemson University in January 2024. Go Tigers!

• 10/23: Our paper "PATROL: Privacy-Oriented Pruning for Collaborative Inference Against Model Inversion Attacks" has been accepted by WACV 2024. Congratulations, Shiwei!

• 10/23: I gave a talk at Temple University, Department of Electrical and Computer Engineering. 

• 08/23: Our project on privacy-preserving collaboration on large neural networks is funded by Accenture. Thanks, Accenture!

• 08/23: Our project on case analysis for security education is funded by NSF (PI: Cai Yu). Thanks, NSF!

• 05/23: I am honored to receive an exceptional “Average of 7 Dimensions” student evaluation score (top-10%) for Spring Semester 2023 at Michigan Tech. 

• 04/23: Our paper "Distributed Pruning Towards Tiny Neural Networks in Federated Learning" has been accepted by IEEE International Conference on Distributed Computing Systems (ICDCS 2023). Congratulations, Hong!

• 03/23: I am honored to serve as the PC of NeurIPS, ICML, ICLR 2023.

• 12/22: Our paper "Cascade Vertical Federated Learning Towards Straggler Mitigation and Label Privacy over Distributed Labels" has been accepted by IEEE Transactions on Big Data. 

• 10/22: I am honored to serve as the TPC member of ICDCS 2023. Please consider submitting your paper. CFP 

• 08/22: I am honored to serve as Associate Editor of IEEE Transactions on Neural Networks and Learning Systems (TNNLS).

• 08/22: Our paper "Shapley Explainer - An Interpretation Method for GNNs Used in SDN" has been accepted by GLOBECOM 2022.

• 07/22: We received an NSF Grant ($500K, PI: Xiaoyong Yuan) on Privacy-Preserving On-Device Intelligence. Thanks, NSF!

• 06/22: Our team participated in MTU Summer Youth program on AIoT.

• 06/22: We received an NSF Grant ($409K, Lead PI: Xiaoyong Yuan) on AIoT. Thanks, NSF! 

• 05/22: I received ORAU Ralph E. Powe Junior Faculty Enhancement Award. Thanks, ORAU!

• 04/22: I serve as a topic editor for "Rising Stars in Image Processing 2022" in Frontiers. Check out the CFP.

• 04/22: I received MTU Institute of Computing and Cybersystems (ICC) Achievement Award. Thanks, ICC!

• 04/22: Our paper "FedZKT: Zero-Shot Knowledge Transfer towards Resource-Constrained Federated Learning with Heterogeneous On-Device Models" has been accepted by IEEE International Conference on Distributed Computing Systems (ICDCS) 2022, with an acceptance rate 19.9%.

• 03/22: Our paper, Pay "Attention" to Adverse Weather: Weather-aware Attention-based Object Detection, has been accepted by ICPR 2022. Congratulations to Saket!

• 03/22: Our paper "Cascade Vertical Federated Learning" has been accepted by ICME 2022.

• 02/22: Our paper "Membership Inference Attacks and Defenses in Neural Network Pruning" has been accepted by USENIX Security Symposium 2022.

• 10/21: Our paper "Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection" has been accepted by IEEE Transactions on Neural Networks and Learning Systems (TNNLS).  

• 07/21: We received NSF Medium Grant ($399K, PI: Xiaoyong Yuan) on collaborative machine learning. Thanks, NSF!

• 06/21: Our paper "Beyond Class-Level Privacy Leakage: Breaking Record-Level Privacy in Federated Learning" has been accepted by IEEE Internet of Things Journal. 

• 05/21: We have been granted a patent for DL-based API risk detection by USPTO.

• 05/21: I will serve as a TPC member for IEEE International Conference on Machine Learning and Applications (ICMLA) 2021. Check out the CFP.

• 05/21: I received the MTU Research Excellence Fund (REF) Award ($29.9K) as a PI on our autonomous driving research.

• 11/20: Our research was funded ($3.5K) by ICC from MTU about AI privacy on mobile phones (PI: Xiaoyong Yuan, Co-PI: Bo Chen). 

• 10/20: Our research was funded ($20K) by the College of Engineering (CoE) from MTU about COVID-19 activity management (PI: Lan Zhang, Co-PI: Xiaoyong Yuan).

• 08/20: I am honored to serve as Publicity Chair and Sponsor Chair for NeurIPS-20 Workshop on Scalability, Privacy, and Security in Federated Learning (SpicyFL). Submission deadline: Oct 12, 2020. Check out the CFP.

• 07/2020: Our paper "Connecting Web Event Forecasting with Anomaly Detection: A Case Study on Enterprise Web Applications Using Self-Supervised Neural Networks" has been accepted by SecureComm 2020.

• 06/20: I am honored to serve as Guest Editor for a Special Issue of Computer Networks, focusing on "Artificial Intelligence Techniques for Autonomous Moving Platforms (AMP) in 5G and Beyond." Check out the CFP.

• 06/20: I am honored to serve as Web Chair for the First EAI International Conference on Applied Cryptography in Computer and Communications (AC3). Submission deadline: Nov 15, 2020. Check out the CFP.

• 03/2020: Our paper "A Praise for Defensive Programming: Leveraging Uncertainty for Effective Malware Mitigation" has been accepted by Transactions on Dependable and Secure Computing (TDSC).

• 02/2020: Our paper “Adaptive Adversarial Attack on Scene Text Recognition” has been accepted to INFOCOM Workshop BigSecurity 2020.

• 10/2019: I gave two talks at NSF CBL semiannual IAB meeting, Gainesville, Florida: "DeepCloud: Managing Deep Learning Lifecycle" and "FedSec: Federated Learning Security Attacks and Defenses".

• 09/2019: New paper "Adversarial Examples: Attacks and Defenses for Deep Learning" is published in IEEE Transactions on Neural Networks and Learning Systems. (Top popular articles)

• 05/2019: I gave a talk at NSF CBL semiannual IAB meeting, Kansas City, Missouri: "FedSec: Federated Learning Security Attacks and Defenses".

• 12/2018: Our paper "Generalized Batch Normalization: Towards Accelerating Deep Neural Networks" will be appeared at AAAI 2019.

Selected Publications (Full publication list)

• [Usenix Security] Xiaoyong Yuan, Lan Zhang, "Membership Inference Attacks and Defenses in Neural Network Pruning," USENIX Security Symposium, 2022. arXiv, Github, acceptance rate 18%.

• [ICDCS] Lan Zhang, Dapeng Wu, Xiaoyong Yuan, "FedZKT: Zero-Shot Knowledge Transfer towards Resource-Constrained Federated Learning with Heterogeneous On-Device Models," IEEE International Conference on Distributed Computing Systems, 2022. arXiv, acceptance rate 19.9%.

• [TNNLS] Ruimin Sun*, Xiaoyong Yuan*, Pan He, Qile Zhu, Aokun Chen, Andre Gregio, Daniela Oliveira, Xiaolin Li, "Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection," IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021. arXiv

• [TDSC] Ruimin Sun, Marcus Botacin, Nikolaos Sapountzis, Xiaoyong Yuan, Matt Bishop, Donald E Porter, Xiaolin Li, Andre Gregio, Daniela Oliveira, "A Praise for Defensive Programming: Leveraging Uncertainty for Effective Malware Mitigation," IEEE Transactions on Dependable and Secure Computing (TDSC), 2020.

• [SecureComm] Xiaoyong Yuan, Lei Ding, Malek Ben Salem, Xiaolin Li, Dapeng Wu, "Connecting Web Event Forecasting with Anomaly Detection: A Case Study on Enterprise Web Applications Using Self-Supervised Neural Networks," EAI International Conference on Security and Privacy in Communication Networks (SecureComm), 2020.

• [AAAI] Xiaoyong Yuan*, Zheng Feng*, Matthew Norton, Xiaolin Li, “Generalized Batch Normalization: Towards Accelerating Deep Neural Networks,” AAAI, 2019 (acceptance rate 16.2%).

• [TNNLS] Xiaoyong Yuan, Pan He, Qile Zhu, Xiaolin Li, “Adversarial Examples: Attacks and Defenses for Deep Learning,” IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2019. arXiv, Github (Top-3 Popular Articles in TNNLS, Oct. 2019).

• [CLOUD] Xiaoyong Yuan, TANG Hongyan, LI Ying, JIA Tong, LIU Tiancheng, WU Zhonghai, “A Competitive Penalty Model for Availability Based Cloud SLA,” The 8th IEEE International Conference on Cloud Computing (CLOUD), New York, US, Jun. 27 - Jul. 2, 2015.

• [COMPSAC] Xiaoyong Yuan, LI Ying, JIA Tong, LIU Tiancheng, WU Zhonghai, “An Analysis on Availability Commitment and Penalty in Cloud SLA,” The 39th Annual International Computers, Software & Applications Conference (COMPSAC), Taiwan, Jul. 1-5, 2015. PDF

• [CLOUDCOM] Xiaoyong Yuan, LI Ying, WU Zhonghai, LIU Tiancheng, “Dependability Analysis on OpenStack IaaS Cloud: Bug Analysis and Fault Injection,” The 6th IEEE International Conference on Cloud Computing Technology and Science (CLOUDCOM), Singapore, Dec. 15-18, 2014. PDF

*(equal contribution)