Publikationer av Sheng Liu
Refereegranskade
Artiklar
[1]
S. Liu et al., "AFM3D : an asynchronous federated meta-learning framework for driver distraction detection," IEEE transactions on intelligent transportation systems (Print), vol. 25, no. 8, s. 9659-9674, 2024.
[2]
L. You et al., "AiFed: An Adaptive and Integrated Mechanism for Asynchronous Federated Data Mining," IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 9, s. 4411-4427, 2024.
[3]
L. You et al., "Federated and Asynchronized Learning for Autonomous and Intelligent Things," IEEE Network, vol. 38, no. 2, s. 286-293, 2024.
[4]
L. You et al., "A Triple-Step Asynchronous Federated Learning Mechanism for Client Activation, Interaction Optimization, and Aggregation Enhancement," IEEE Internet of Things Journal, vol. 9, no. 23, s. 24199-24211, 2022.
[5]
H. Qu et al., "Adaptation and Learning to Learn (ALL): An Integrated Approach for Small-Sample Parking Occupancy Prediction," Mathematics, vol. 10, no. 12, 2022.
[6]
S. Liu, Q. Chen och L. You, "Fed2A: Federated Learning Mechanism in Asynchronous and Adaptive Modes," Electronics, vol. 11, no. 9, s. 1393-1393, 2022.
Konferensbidrag
[7]
S. Liu, L. You och Y. Zhou, "FedRC : representational consistency guided model uploading mechanism for asynchronous federated learning," i Mobile and Ubiquitous Systems : Computing, Networking and Services, 2023, s. 239-256.
[8]
G. Chen et al., "FedRSM: Representational-Similarity-Based Secured Model Uploading for Federated Learning," i International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), 1-3 Nov. 2023, 2023, s. 189-196.
[9]
S. Liu et al., "AFMeta : asynchronous federated meta-learning with temporally weighted aggregation," i 2022 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Autonomous & Trusted Vehicles, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse (SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse), 2022, s. 641-648.
[10]
Q. Chen et al., "TWAFR-GRU : An Integrated Model for Real-time Charging Station Occupancy Prediction," i Proceedings 2022 IEEE Smartworld, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, 2022, s. 1611-1618.
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2024-11-03 03:52:23