Junxiao Wang (王军晓)

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Postdoctoral Researcher
Department of Computing
The Hong Kong Polytechnic University
Hung Hom, Kowloon, Hong Kong SAR
Work Email: email
Personal Email: email

Find me at  linkedin github google scholar

About Me

I currently hold the position of Postdoctoral Researcher at the PolyU Edge Intelligence Laboratory (PEIL) of The Hong Kong Polytechnic University, working with Prof. Song Guo.

In 2018-2019, I was a Visiting Researcher at the Networks Research Group of Queen Mary University of London, working with Prof. Steve Uhlig. I completed my PhD at Dalian University of Technology in 2020, where my mentor was Prof. Keqiu Li. Prior to that, I earned my MEng and BE degrees in 2017, 2014.

Research Interests

I'm broadly interested in machine learning and systems with a special focus on federated learning, trustworthy machine learning and networking.

Recent Professional Activities

  • Postdoctoral Researcher, working with Prof. Di Wang, at Division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), 2023.03

  • Invited Talk, in Ritsumeikan University and CCF Dalian International Seminar, 2022.03

  • Postdoctoral Researcher, working with Prof. Song Guo, at Department of Computing (COMP), The Hong Kong Polytechnic University (PolyU), 2021.03-2023.03

  • Session Chair for IEEE International Conference on Parallel and Distributed Systems (ICPADS), 2019.12

  • Visiting Researcher, working with Prof. Steve Uhlig, at School of Electronic Engineering and Computer Science (EECS), Queen Mary University of London (QMUL), 2018.10-2019.10

News

  • Jan 2023   Our paper entitled "pFedPrompt: Learning Personalized Prompt for Vision-Language Models in Federated Learning" was accepted by ACM WWW2023.

  • Nov 2022   Our paper entitled "Demystify Self-Attention in Vision Transformers from a Semantic Perspective: Analysis and Application" was available in arXiv pre-print.

  • Nov 2022   Our paper entitled "PMR: Prototypical Modal Rebalance for Multimodal Learning" was available in arXiv pre-print.

  • Nov 2022   Our paper entitled "FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers" was available in arXiv pre-print.

  • Aug 2022   Our paper entitled "PromptFL: Let Federated Participants Cooperatively Learn Prompts Instead of Models — Federated Learning in Age of Foundation Model" was available in arXiv pre-print.

  • Aug 2022   Our paper entitled "Federated Unlearning: Guarantee the Right of Clients to Forget" was accepted by IEEE Network.

  • Apr 2022   Our paper entitled "Efficient Integrity Authentication Scheme for Large-scale RFID Systems" was accepted by IEEE Transactions on Mobile Computing.

  • Apr 2022   Our paper entitled "A Survey on Gradient Inversion: Attacks, Defenses and Future Directions" was accepted by Survey track of IJCAI2022.

  • Feb 2022   Our paper entitled "Vertical Machine Unlearning: Selectively Removing Sensitive Information From Latent Feature Space" was available in arXiv pre-print.

  • Jan 2022   Our paper entitled "Federated Unlearning via Class-Discriminative Pruning" was accepted by ACM WWW2022.

  • Dec 2021   Our paper entitled "Protect Privacy from Gradient Leakage Attack in Federated Learning" was accepted by IEEE INFOCOM2022.

  • Oct 2021   Our paper entitled "Federated Unlearning via Class-Discriminative Pruning" was available in arXiv pre-print.

  • Feb 2020   Our paper entitled "Click-UP: Toward the Software Upgrade of Click-Based Modular Network Function" was accepted by IEEE Systems Journal.

  • Jun 2018   Our paper entitled "CLICK-UP: Towards Software Upgrades of Click-driven Stateful Network Elements" was accepted by Demo track of ACM SIGCOMM2018.