CV

Employment

• Predoctoral Researcher in KTH Royal Institute of Technology, Sweden, Jul. 2024 - Now
  • My KTH profile: https://www.kth.se/profile/shengliu/

Education

• Bachelor’s Degree in Sun Yat-sen University, China, Sep. 2017 - Jun. 2021
  • Major: Traffic Engineering, School of Intelligent Systems Engineering
  • Minor: Logic, Department of Philosophy
  • Thesis Title: Fatigue Driving Detection Algorithm Based on Federated Learning and Multi-source Heterogeneous Data, supervised by Associate Prof. Junbo Wang
• Master’s Degree in Sun Yat-sen University, China, Sep. 2021 - Jun. 2024
  • Major: Traffic Information Engineering & Control, School of Intelligent Systems Engineering
  • Advisor: Associate Prof. Linlin You
  • Thesis Title: Research of Asynchronous Federated Learning Mechanism for Driver Distraction Detection
• Doctoral’s Degree in KTH Royal Institute of Technology, Sweden, Jul. 2024 - Now KTH profile is here
  • Major: Information and Communication Technology, Division of Software and Computer Systems
  • Supervisor: Prof. Panos Papadimitratos

Project Experience

• Research and Application of Asynchronous Federated Learning Model for 5G Heterogeneous Intelligent Terminal Collaboration
  • National Natural Science Foundation for Young Scholars, Participant, 2021.09 - 2024.06
  • Considering the heterogeneous IoT environment and the dynamic change of resources (data), I study the client selection, communication optimization, and aggregation enhancement algorithms for Asynchronous Federated Learning (AFL) based on the Deep Neural Networks (DNNs) and information theory to improve model performance and learning efficiency significantly.
• Research of Autonomous Transportation System Architecture
  • National Key R&D Program, Participant, 2021.09 - 2024.06
  • Towards the data islands and personalization demands, I build an asynchronous federated meta-learning framework for efficient collaboration in smart mobility scenarios. Such that, a meta-model with rapid adaptation ability can be trained to realize high-performing charging station occupancy prediction and driver distraction detection.

Publications

Fed2A: Federated Learning Mechanism in Asynchronous and Adaptive Modes

S Liu, Q Chen, and L You, "Fed2A: Federated Learning Mechanism in Asynchronous and Adaptive Modes", Electronics, 11(9):1393, Apr 2022, doi: 10.3390/electronics11091393.

Adaptation and Learning to Learn (ALL): An Integrated Approach for Small-Sample Parking Occupancy Prediction

H Qu, S Liu, J Li, Y Zhou, and R Liu, "Adaptation and Learning to Learn (ALL): An Integrated Approach for Small-Sample Parking Occupancy Prediction", Mathematics, 10(12):2039, Jun 2022, doi: 10.3390//math10122039.

A Triple-step Asynchronous Federated Learning Mechanism for Client Activation, Interaction Optimization, and Aggregation Enhancement

L. You, S. Liu, Y. Chang and C. Yuen, "A Triple-Step Asynchronous Federated Learning Mechanism for Client Activation, Interaction Optimization, and Aggregation Enhancement," IEEE Internet of Things Journal, vol. 9, no. 23, pp. 24199-24211, Dec 2022, doi: 10.1109/JIOT.2022.3188556.

Federated Architecture for Personal Mobility Service in Autonomous Transportation System

J He, Z Deng, S Liu, L You, M Zhou, M Cai, and S Cheng, "Federated Architecture for Personal Mobility Service in Autonomous Transportation System",COTA International Conference of Transportation Professionals, Jul 2022: 23-33, doi: 10.1061/9780784484265.003.

Improving Parking Occupancy Prediction in Poor Data Conditions Through Customization and Learning to Learn

H Qu, S Liu, Z Guo, L You, and J Li, "Improving Parking Occupancy Prediction in Poor Data Conditions Through Customization and Learning to Learn", International Conference on Knowledge Science, Engineering and Management, 159-172, Jul 2022, doi: 10.1007/978-3-031-10983-6_13.

TWAFR-GRU: An Integrated Model for Real-Time Charging Station Occupancy Prediction

Q Chen, S Liu, H Qu, R Zhu, and L You, "TWAFR-GRU: An Integrated Model for Real-Time Charging Station Occupancy Prediction", IEEE International Conference on Ubiquitous Intelligence and Computing, 1611-1618, Dec 2022, doi: 10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00233.

AFMeta: Asynchronous Federated Meta-learning with Temporally Weighted Aggregation

S Liu, H Qu, Q Chen, W Jian, R Liu, and L You, "AFMeta: Asynchronous Federated Meta-learning with Temporally Weighted Aggregation", IEEE International Conference on Ubiquitous Intelligence and Computing, 641-648, Dec 2022, doi: 10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00100.

ICMFed: An Incremental and Cost-Efficient Mechanism of Federated Meta-Learning for Driver Distraction Detection

Z Guo, L You, S Liu, J He, and B Zuo, "ICMFed: An Incremental and Cost-Efficient Mechanism of Federated Meta-Learning for Driver Distraction Detection", Mathematics, 11(8):1867, Apr 2023, doi: 10.3390/math11081867.

Federated and Asynchronized Learning for Autonomous and Intelligent Things

L You, S Liu, B Zuo, C Yuen, D Niyato, and H Vincent Poor, "Federated and Asynchronized Learning for Autonomous and Intelligent Things", IEEE Network, vol. 38, no. 2, pp. 286-293, Mar 2024, doi: 10.1109/MNET.2023.3321519.

FedRSM: Representational-Similarity-Based Secured Model Uploading for Federated Learning

G Chen, S Liu, X Yang, T Wang, L You, and F Xia, "FedRSM: Representational-Similarity-Based Secured Model Uploading for Federated Learning", IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 189-196, Nov, 2023, doi: 10.1109/TrustCom60117.2023.00046.

FedRC: Representational Consistency Guided Model Uploading Mechanism for Asynchronous Federated Learning

S Liu, L You, and Y Zhou, "FedRC: Representational Consistency Guided Model Uploading Mechanism for Asynchronous Federated Learning", EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, 239-256, Jul 2024, doi: 10.1007/978-3-031-63989-0_12.

AiFed: An Adaptive and Integrated Mechanism for Asynchronous Federated Data Mining

L You, S Liu, T Wang, B Zuo, Y Chang, and C Yuen, "AiFed: An Adaptive and Integrated Mechanism for Asynchronous Federated Data Mining", IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 9, pp. 4411-4427, Sep 2024, doi: 10.1109/TKDE.2023.3332770.

AFM3D: An Asynchronous Federated Meta-learning Framework for Driver Distraction Detection

S Liu, L You, R Zhu, B Liu, R Liu, H Yu, and C Yuen, "AFM3D: An Asynchronous Federated Meta-learning Framework for Driver Distraction Detection", IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 8, pp. 9659-9674, Aug 2024, doi: 10.1109/TITS.2024.3357138.

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 Engineering Applications of Artificial Intelligence (EAAI)
5. Reviewer for Cluster Computing
6. Reviewer for IEEE Internet of Things Journal (IEEE IoTJ)
7. Reviewer for The Journal of Supercomputing (TJSC)
8. Reviewer for Scientific Reports
9. Reviewer for IEEE Access

Teaching

Traffic Big Data Technology

Network and Communication Principles

Traffic Information Engineering

Traffic Big Data Technology

Cybersecurity and Privacy Summer School

Advanced Networked Systems Security