Publications

You can also find my articles on my Google Scholar profile.

AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora

Published in The 64th Annual Meeting of the Association for Computational Linguistics (ACL), 2026

This paper is about autonomous knowledge graph construction through dynamic schema induction from web-scale corpora.

Recommended citation: Jiaxin Bai, Wei Fan, Qi Hu, Qing Zong, Chunyang Li, Hong Ting Tsang, Hongyu Luo, Yauwai Yim, Haoyu Huang, Xiao Zhou, Feng Qin, Tianshi Zheng, Xi Peng, Xin Yao, Huiwen Yang, Leijie Wu, JI Yi, Gong Zhang, Renhai Chen, and Yangqiu Song. "AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora." The 64th Annual Meeting of the Association for Computational Linguistics (ACL). 2026: 20557-20584. https://aclanthology.org/2026.acl-long.942/

PP-OpenNet: Privacy-Preserved Open Set Classification for Network Traffic

Published in IEEE International Conference on Computer Communications (INFOCOM) (CCF-A), 2026

This paper is about privacy-preserved open set classification for network traffic.

Recommended citation: Jingze Zhang, Leijie Wu, Xi Peng, Qingqing Yang, Ruilun Liu, and Hong Xu. "PP-OpenNet: Privacy-Preserved Open Set Classification for Network Traffic." IEEE International Conference on Computer Communications (INFOCOM). 2026: 1-6. https://ieeexplore.ieee.org/document/11571190

QuickDrop: Efficient Federated Unlearning via Synthetic Data Generation

Published in 25th International Middleware Conference (Middleware 2024), 2024

This paper is about efficient federated unlearning.

Recommended citation: Leijie Wu, Yaohong Ding, Akash Dhasade, Martijn De Vos, Anne-Marie Kermarrec, and Song Guo. "QuickDrop: Efficient Federated Unlearning via Synthetic Data Generation." 25th International Middleware Conference (Middleware 2024). 2024. https://dl.acm.org/doi/10.1145/3652892.3700764

Rethinking Personalized Client Collaboration in Federated Learning

Published in IEEE Transactions on Mobile Computing (TMC) (CCF-A), 2024

This paper is about personalized federated learning.

Recommended citation: Leijie Wu, Song Guo, Yaohong Ding, Junxiao Wang, Wenchao Xu, Yufeng Zhan, and Anne-Marie Kermarrec. "Rethinking Personalized Client Collaboration in Federated Learning." IEEE Transactions on Mobile Computing (TMC). 2024. https://ieeexplore.ieee.org/document/10517642

Chiron: A Robustness-Aware Incentive Scheme for Edge Learning Via Hierarchical Reinforcement Learning

Published in IEEE Transactions on Mobile Computing (TMC) (CCF-A), 2024

This paper is about incentive mechanism design in federated learning.

Recommended citation: Yi Liu, Song Guo, Yufeng Zhan, Leijie Wu, Zicong Hong, and Qihua Zhou. "Chiron: A Robustness-Aware Incentive Scheme for Edge Learning Via Hierarchical Reinforcement Learning." IEEE Transactions on Mobile Computing (TMC). 2024. https://ieeexplore.ieee.org/document/10382540

Long-term Adaptive VCG Auction Mechanism for Sustainable Federated Learning with Periodical Client Shifting

Published in IEEE Transactions on Mobile Computing (TMC) (CCF-A), 2023

This paper is about the incentive mechanism design for federated learning.

Recommended citation: Leijie Wu, Song Guo, Yi Liu, Zicong Hong, Yufeng Zhan, and Wenchao Xu. "Long-term Adaptive VCG Auction Mechanism for Sustainable Federated Learning with Periodical Client Shifting." IEEE Transactions on Mobile Computing (TMC). 2023. https://ieeexplore.ieee.org/document/10255325

On Knowledge Editing in Federated Learning: Perspectives, Challenges, and Future Directions

Published in arXiv, 2023

This paper is about knowledge editing in federated learning.

Recommended citation: Leijie Wu, Song Guo, Junxiao Wang, Zicong Hong, Jie Zhang, and Jingren Zhou. "On Knowledge Editing in Federated Learning: Perspectives, Challenges, and Future Directions." arXiv. 2023. https://arxiv.org/abs/2306.01431

Demystify Self-Attention in Vision Transformers from a Semantic Perspective: Analysis and Application

Published in arXiv, 2022

This paper is about interpretability of self-Attention mechanism in vision transformers.

Recommended citation: Leijie Wu, Song Guo, Yaohong Ding, Junxiao Wang, Wenchao Xu, Jie Zhang, and Richard Yida Xu. "Demystify Self-Attention in Vision Transformers from a Semantic Perspective: Analysis and Application." arXiv. 2022. https://arxiv.org/abs/2211.08543

Federated Unlearning: Guarantee the Right of Clients to Forget

Published in IEEE Network (JCR-Q1), 2022

This paper is about the federated unlearning.

Recommended citation: Leijie Wu, Song Guo, Junxiao Wang, Zicong Hong, Jie Zhang, and Yaohong Ding. "Federated Unlearning: Guarantee the Right of Clients to Forget." IEEE Network. 2022. https://ieeexplore.ieee.org/document/9964015/

Sustainable Federated Learning with Long-term Online VCG Auction Mechanism

Published in IEEE 42nd International Conference on Distributed Computing Systems (ICDCS) (CCF-B), 2022

This paper is about the long-term incentive mechanism design for federated learning.

Recommended citation: Leijie Wu, Song Guo, Yi Liu, Zicong Hong, Yufeng Zhan, and Wenchao Xu. "Sustainable Federated Learning with Long-term Online VCG Auction Mechanism." IEEE 42nd International Conference on Distributed Computing Systems (ICDCS). 2022: 895-905. https://ieeexplore.ieee.org/document/9912254

Incentive-Driven Long-term Optimization for Edge Learning by Hierarchical Reinforcement Mechanism

Published in IEEE 41st International Conference on Distributed Computing Systems (ICDCS) (CCF-B), 2021

This paper is about the incentive-driven long-term optimization for edge learning.

Recommended citation: Yi Liu*, Leijie Wu*, Yufeng Zhan, Song Guo, and Zicong Hong (* indicates co-first authors with equal contribution). "Incentive-Driven Long-term Optimization for Edge Learning by Hierarchical Reinforcement Mechanism." IEEE 41st International Conference on Distributed Computing Systems (ICDCS). 2021: 35-45. https://ieeexplore.ieee.org/document/9546525

L4L: Experience-Driven Computational Resource Control in Federated Learning

Published in IEEE Transactions on Computers (TC) (CCF-A), 2021

This paper is about using deep reinforemecent learning to control the resource distribution in federated learning.

Recommended citation: Yufeng Zhan, Peng Li, Leijie Wu, and Song Guo. "L4L: Experience-Driven Computational Resource Control in Federated Learning." IEEE Transactions on Computers (TC). 2021. https://ieeexplore.ieee.org/document/9384231

A Survey of Incentive Mechanism Design for Federated Learning

Published in IEEE Transactions on Emerging Topics in Computing (TETC), 2021

This paper is about the incentive mechanism design survey in the scenario of federated learning.

Recommended citation: Yufeng Zhan, Jie Zhang, Zicong Hong, Leijie Wu, Peng Li, and Song Guo. "A Survey of Incentive Mechanism Design for Federated Learning." IEEE Transactions on Emerging Topics in Computing (TETC). 2021. https://ieeexplore.ieee.org/abstract/document/9369019

Leijie (Eric) Wu

Publications