Kangkang Sun

Kangkang Sun

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

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Posts

Future Blog Post

less than 1 minute read

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This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

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Blog Post number 2

less than 1 minute read

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Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

projects

publications

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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