FedIBD: a federated learning framework in asynchronous mode for imbalanced data.
Appl. Intell., January, 2025
AFM3D: An Asynchronous Federated Meta-Learning Framework for Driver Distraction Detection.
IEEE Trans. Intell. Transp. Syst., August, 2024
Aggregating intrinsic information to enhance BCI performance through federated learning.
Neural Networks, 2024
Federated Knowledge Transfer Fine-tuning Large Server Model with Resource-Constrained IoT Clients.
CoRR, 2024
EEG-Deformer: A Dense Convolutional Transformer for Brain-computer Interfaces.
CoRR, 2024
Towards Interpretable Federated Learning.
CoRR, 2023
Federated Graph Neural Networks: Overview, Techniques and Challenges.
CoRR, 2022
AFMeta: Asynchronous Federated Meta-learning with Temporally Weighted Aggregation.
Proceedings of the IEEE Smartworld, 2022
Joint estimation of low-rank components and connectivity graph in high-dimensional graph signals: Application to brain imaging.
Signal Process., 2021
IoT Based Experimental Study to Modify Water Consumption Behavior of Domestic Users.
Proceedings of the Smart Grid and Innovative Frontiers in Telecommunications, 2018
Simultaneous low-rank component and graph estimation for high-dimensional graph signals: Application to brain imaging.
Proceedings of the 2017 IEEE International Conference on Acoustics, 2017
Dimensionality reduction of brain imaging data using graph signal processing.
Proceedings of the 2016 IEEE International Conference on Image Processing, 2016