Sheng Liu

Sheng Liu

PhD student, Networked Systems Security (NSS) group, KTH Royal Institute of Technology

Hi, I’m Sheng Liu (刘晟)

I am a PhD student in Networked Systems Security (NSS) Group at KTH Royal Institute of Technology, Sweden, supervised by Prof. Panos Papadimitratos. My KTH profile is here. I am funded by WASP, i.e., Wallenberg AI, Autonomous Systems and Software Program.

I received my master’s degree and my B.Eng. degree from the School of Intelligent Systems Engineering, Sun Yat-sen University, China, in 2024 and 2021, respectively.

I’m always delighted to collaborate, discuss, chat, and learn from different minds! So please feel free to contact me via email (shengliu@kth.se), WeChat (nobody-910), or any other convenient means of communication!

My current research interests focus on the intersection between:

  • federated learning
  • autonomous systems
  • security and privacy

News

  1. [Jul. 2024] I arrive at KTH, Stockholm, Sweden, as a predoctoral researcher at the School of Electrical Engineering and Computer Science (EECS). A new journey!
  2. [Jun. 2024] I am honored to be awarded the Outstanding Graduate of Sun Yat-sen University!
  3. [Jan. 2024] My journal paper titled “AFM3D: An Asynchronous Federated Meta-learning Framework for Driver Distraction Detection” is accepted by IEEE T-ITS!
  4. [Nov. 2023] My journal paper titled “AiFed: An Adaptive and Integrated Mechanism for Asynchronous Federated Data Mining” is accepted by IEEE TKDE!
  5. [Nov. 2023] My journal paper titled “A Triple-step Asynchronous Federated Learning Mechanism for Client Activation, Interaction Optimization, and Aggregation Enhancement” published in IEEE IoTJ is selected as the Shenzhen 3rd Excellent Science & Technology Academic Paper!
  6. [Oct. 2023] I am honored to receive the National Scholarship for Postgraduates!
  7. [Oct. 2023] My conference paper titled “FedRC: Representational Consistency Guided Model Uploading Mechanism for Asynchronous Federated Learning” is accepted by EAI MobiQuitous!

Publications

  1. Liu S, You L, Zhu R, Liu B, Liu R, Yu H, and Yuen C. AFM3D: An Asynchronous Federated Meta-learning Framework for Driver Distraction Detection[J], IEEE Transactions on Intelligent Transportation Systems, 2024, 25(8): 9659-9674. (JCR Q1, IF 8.5)

  2. You L (Advisor), Liu S, Wang T, Zuo B, Chang Y, and Yuen C. AiFed: An Adaptive and Integrated Mechanism for Asynchronous Federated Data Mining[J], IEEE Transactions on Knowledge and Data Engineering, 2024, 36(9): 4411-4427. (JCR Q1, CCF-A, IF 8.9)

  3. You L (Advisor), Liu S, Zuo B, Yuen C, Niyato D, and Poor H. V. Federated and Asynchronized Learning for Autonomous and Intelligent Things[J], IEEE Network, 2024, 38(2): 286-293. (JCR Q1, IF 9.3)

  4. You L (Advisor), Liu S, Chang Y, and Yuen C. A Triple-step Asynchronous Federated Learning Mechanism for Client Activation, Interaction Optimization, and Aggregation Enhancement[J]. IEEE Internet of Things Journal, 2022, 9(23): 24199-24211. (JCR Q1, IF 10.6, Shenzhen Excellent Academic Paper Award)

  5. Liu S, Chen Q, and You L. Fed2A: Federated Learning Mechanism in Asynchronous and Adaptive Modes[J]. Electronics, 2022, 11(9): 1393. (JCR Q2, IF 2.9)

  6. Liu S, Zhou Y, and You L. FedRC: Representational Consistency Guided Model Uploading Mechanism for Asynchronous Federated Learning[C]// EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous), 2023: 239-256. (CCF-C, Presenter)

  7. Liu S, Qu H, Chen Q, Jian W, Liu R, and You L. AFMeta: Asynchronous Federated Meta-learning with Temporally Weighted Aggregation[C]// IEEE International Conference on Ubiquitous Intelligence and Computing (UIC), 2022: 641-648. (CCF-C, 17.9% acceptance rate, Presenter)

  8. Qu H, Liu S, Li J, Zhou Y, and Liu R. Adaptation and Learning to Learn (ALL): An Integrated Approach for Small-Sample Parking Occupancy Prediction[J]. Mathematics, 2022, 10(12): 2039. (JCR Q1, IF 2.4)

  9. Chen G, Liu S, Yang X, You L, and Xia F. FedRSM: Representational-Similarity-Based Secured Model Uploading for Federated Learning[C]// IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 2023: 189-196. (Accepted, CCF-C, 30% acceptance rate)

  10. Qu H, Liu S, Guo Z, You L, and Li J. Improving Parking Occupancy Prediction in Poor Data Conditions Through Customization and Learning to Learn[C]//International Conference on Knowledge Science, Engineering and Management (KSEM). Springer, Cham, 2022: 159-172. (CCF-C, 33.9% acceptance rate)

  11. Chen Q, Liu S, Qu H, Zhu R, and You L. TWAFR-GRU: An Integrated Model for Real-Time Charging Station Occupancy Prediction[C]// IEEE International Conference on Ubiquitous Intelligence and Computing (UIC), 2022: 1611-1618. (CCF-C, 24.5% acceptance rate)

  12. Guo Z, You L (Advisor), Liu S, He J, and Zuo B. ICMFed: An Incremental and Cost-Efficient Mechanism of Federated Meta-Learning for Driver Distraction Detection[J]. Mathematics, 2023, 11(8): 1867. (JCR Q1, IF 2.4)

Awards

  1. Outstanding Graduate of Sun Yat-sen University, 2024
  2. National Scholarship for Postgraduates, 2023
  3. Shenzhen Excellent Academic Paper Award, 2023
  4. Samsung Scholarship (Only one person is selected in the school), 2022
  5. Guangdong Guangda Scholarship (Only five persons are selected in the school), 2021

Academic Services

  1. Reviewer for International Conference on Learning Representations (ICLR)
  2. Reviewer for IEEE Transactions on Parallel and Distributed Systems (IEEE TPDS)
  3. Reviewer for IEEE Transactions on Knowledge and Data Engineering (IEEE TKDE)
  4. Reviewer for IEEE Internet of Things Journal (IEEE IoTJ)
  5. Reviewer for The Journal of Supercomputing (TJSC)
  6. Reviewer for Scientific Reports
  7. Reviewer for IEEE Access

Teaching Experience

  1. Teaching Assistant for EP2510 Advanced Networked Systems Security, PG Level, KTH, Sweden, 2024 Fall Course web
  2. Teaching Assistant for EP250V Cybersecurity and Privacy Summer School, PG Level, KTH, Sweden, 2024 Spring Course web
  3. Teaching Assistant for Traffic Big Data Technology, UG Level, Sun Yat-sen University, China, 2022/23 Spring
  4. Teaching Assistant for Traffic Information Engineering, PG Level, Sun Yat-sen University, China, 2022/23 Spring
  5. Teaching Assistant for Network and Communication Principles, UG Level, Sun Yat-sen University, China, 2022/23 Fall
  6. Teaching Assistant for Traffic Big Data Technology, UG Level, Sun Yat-sen University, China, 2021/22 Spring

Hobbies

  1. Reading
  2. Volunteering