Published in Middleware24: Proceedings of the 25th International Middleware Conference, 2024
This paper is about efficient federated unlearning.
Recommended citation: Leijie Wu, Yaohong Ding, Akash Dhasade, Martijn De Vos, Anne-marie Kermarrec, Song Guo. "QuickDrop: Efficient Federated Unlearning via Synthetic Data Generation." Middleware24. 2024. https://dl.acm.org/doi/10.1145/3652892.3700764
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
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
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
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
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
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/
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
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
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
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." EEE Transactions on Emerging Topics in Computing (TETC). 2021. https://ieeexplore.ieee.org/abstract/document/9369019