Kangkang Sun

Kangkang Sun

Postdoctoral Researcher, School of Computer Science, Shanghai Jiao Tong University

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Leveraging Digital Twin and DRL for Collaborative Context Offloading in C-V2X Autonomous Driving

Published in IEEE Transactions on Vehicular Technology, 2023

Contribution: This paper combines Mean Field Game (MFG) and Deep Reinforcement Learning to tackle the collaborative context offloading problem in C-V2X Autonomous Driving.

Recommended citation: Kangkang Sun, Jun Wu, Qianqian Pan, Xi Zheng, Jianhua Li and Shui Yu, "Leveraging Digital Twin and DRL for Collaborative Context Offloading in C-V2X Autonomous Driving," in IEEE Transactions on Vehicular Technology, vol. 73, no. 4, pp. 5020-5035, April 2024, doi: 10.1109/TVT.2023.3333243. (First Author, IF: 7.1)
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Toward the Tradeoffs between Privacy, Fairness and Utility in Federated Learning

Published in International Symposium on Emerging Information Security and Applications. Singapore: Springer Nature Singapore (EISA 2024), 2024

Recommended citation: Kangkang Sun, Xiaojin Zhang, Lin Xi, Gaolei Li, Jing Wang and Jianhua Li. Toward the Tradeoffs Between Privacy, Fairness and Utility in Federated Learning[C]//International Symposium on Emerging Information Security and Applications. Singapore: Springer Nature Singapore, 2023: 118-132. (First Author)
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Dynamic Privacy Protection of Federated Learning for Vehicular Digital Twin Networks

Published in International Symposium on Intelligent Computing and Networking 2024 (ISICN 2024), 2024

Recommended citation: Kangkang Sun, Hansong Xu, Xiaojin Zhang, Kun Hua and Jianhua Li. Time-Sensitive Local Differential Privacy-Based Federated Learning for Vehicular Digital Twin Networks[C]//International Symposium on Intelligent Computing and Networking. Cham: Springer Nature Switzerland, 2024: 105-118. (First Author)
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Personalized Privacy-Preserving Distributed Artificial Intelligence for Digital-Twin-Driven Vehicle Road Cooperation

Published in IEEE Internet of Things Journal, 2024

Contribution: This paper focuses on privacy preservation in Digital-Twin-Driven Vehicle Road Cooperation with a time-sensitive PLDP-based FL (TimeSenFLDP) mechanism, which can achieve different privacy levels of the DT model of vehicles over sharing time steps.

Recommended citation: Kangkang Sun, Jun Wu, Ali Kashif Bashir, Jianhua Li, Hansong Xu, Qianqian Pan and Yasser D. Al-Otaibi, "Personalized Privacy-Preserving Distributed Artificial Intelligence for Digital-Twin-Driven Vehicle Road Cooperation," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2024.3389656.(First Author, IF:8.9)
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Joint Top-K Sparsification and Shuffle Model for Communication-Privacy-Accuracy Tradeoffs in Federated-Learning-Based IoV

Published in IEEE Internet of Things Journal, 2024

Contribution: This paper uses the sparsification and Shuffle model to improve the communication-privacy-accuracy tradeoffs in Federated Learning, which is the first study about privacy enhancement based on the Shuffle model in the IoV network.

Recommended citation: Kangkang Sun; Hansong Xu; Kun Hua; Xi Lin; Gaolei Li; Tigang Jiang and Jianhua Li, "Joint Top-K Sparsification and Shuffle Model for Communication-Privacy-Accuracy Tradeoffs in Federated-Learning-Based IoV," in IEEE Internet of Things Journal, vol. 11, no. 11, pp. 19721-19735, 1 June1, 2024, doi: 10.1109/JIOT.2024.3370991. (First Author, IF:8.9)
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Large-Scale Mean-Field Federated Learning for Detection and Defense against Byzantine Attacks in IoT

Published in IEEE Internet of Things Journal, 2024

Contribution: This paper is the first study, based on Mean Field Game (MFG) to detect and defend against Byzantine attacks in Federated Learning.

Recommended citation: Kangkang Sun, Lizheng Liu, Qianqian Pan, Jianhua Li and Ju Wu, "Large-Scale Mean-Field Federated Learning for Detection and Defense: A Byzantine Robustness Approach in IoT," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2024.3409610. (First Author, IF：8.9)
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