Publications

DEFEND: Poisoned Model Detection and Malicious Client Exclusion Mechanism for Secure Federated Learning-based Road Condition Classification

Published in The 41st ACM/SIGAPP Symposium on Applied Computing (SAC), 2026

Recommended citation: S Liu and Panos Papadimitratos, "DEFEND: Poisoned Model Detection and Malicious Client Exclusion Mechanism for Secure Federated Learning-based Road Condition Classification", ACM/SIGAPP Symposium on Applied Computing (SAC), pp. 1486–1495, Mar 2026, doi: 10.1145/3748522.3779807. https://dl.acm.org/doi/10.1145/3748522.3779807

Safeguarding Federated Learning-Based Road Condition Classification

Published in 2025 IEEE Conference on Communications and Network Security (CNS), 2025

Recommended citation: S Liu and Panos Papadimitratos, "Safeguarding Federated Learning-Based Road Condition Classification", IEEE Conference on Communications and Network Security (CNS), pp. 1-9, Sep 2025, doi: 10.1109/CNS66487.2025.11194922. https://ieeexplore.ieee.org/document/11194922

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

Published in IEEE Transactions on Intelligent Transportation Systems, 2024

Recommended citation: 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. https://ieeexplore.ieee.org/document/10423999

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

Published in IEEE Transactions on Knowledge and Data Engineering, 2023

Recommended citation: 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. https://ieeexplore.ieee.org/document/10316646

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

Published in EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, 2023

Recommended citation: 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. https://link.springer.com/chapter/10.1007/978-3-031-63989-0_12

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

Published in IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 2023

Recommended citation: 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. https://ieeexplore.ieee.org/abstract/document/10538768

Federated and Asynchronized Learning for Autonomous and Intelligent Things

Published in IEEE Network, 2023

Recommended citation: 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. https://ieeexplore.ieee.org/document/10274563

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

Published in Mathematics, 2023

Recommended citation: 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. https://www.mdpi.com/2227-7390/11/8/1867

AFMeta: Asynchronous Federated Meta-learning with Temporally Weighted Aggregation

Published in IEEE International Conference on Ubiquitous Intelligence and Computing, 2022

Recommended citation: 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. https://ieeexplore.ieee.org/document/10189596

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

Published in IEEE International Conference on Ubiquitous Intelligence and Computing, 2022

Recommended citation: 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. https://ieeexplore.ieee.org/document/10189531

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

Published in International Conference on Knowledge Science, Engineering and Management, 2022

Recommended citation: 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. https://link.springer.com/chapter/10.1007/978-3-031-10983-6_13

Federated Architecture for Personal Mobility Service in Autonomous Transportation System

Published in COTA International Conference of Transportation Professionals, 2022

Recommended citation: 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. https://ascelibrary.org/doi/abs/10.1061/9780784484265.003

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

Published in IEEE Internet of Things Journal, 2022

Recommended citation: 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. https://ieeexplore.ieee.org/abstract/document/9815310

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

Published in Mathematics, 2022

Recommended citation: 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. https://www.mdpi.com/2227-7390/10/12/2039

Fed2A: Federated Learning Mechanism in Asynchronous and Adaptive Modes

Published in Electronics, 2022

Recommended citation: 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. https://www.mdpi.com/2079-9292/11/9/1393

Sheng Liu

Publications