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All Outputs (13)

A survey on vulnerability of federated learning: A learning algorithm perspective (2024)
Journal Article
Xie, X., Hu, C., Ren, H., & Deng, J. (2024). A survey on vulnerability of federated learning: A learning algorithm perspective. Neurocomputing, 573, Article 127225. https://doi.org/10.1016/j.neucom.2023.127225

Federated Learning (FL) has emerged as a powerful paradigm for training Machine Learning (ML), particularly Deep Learning (DL) models on multiple devices or servers while maintaining data localized at owners’ sites. Without centrali... Read More about A survey on vulnerability of federated learning: A learning algorithm perspective.

FedBoosting: Federated learning with gradient protected boosting for text recognition (2023)
Journal Article
Ren, H., Deng, J., Xie, X., Ma, X., & Wang, Y. (2024). FedBoosting: Federated learning with gradient protected boosting for text recognition. Neurocomputing, 569, Article 127126. https://doi.org/10.1016/j.neucom.2023.127126

Conventional machine learning methodologies require the centralization of data for model training, which may be infeasible in situations where data sharing limitations are imposed due to concerns such as privacy and gradient protect... Read More about FedBoosting: Federated learning with gradient protected boosting for text recognition.

Image restoration with group sparse representation and low‐rank group residual learning (2023)
Journal Article
Cai, Z., Xie, X., Deng, J., Dou, Z., Tong, B., & Ma, X. (2023). Image restoration with group sparse representation and low‐rank group residual learning. IET Image Processing, https://doi.org/10.1049/ipr2.12982

Image restoration, as a fundamental research topic of image processing, is to reconstruct the original image from degraded signal using the prior knowledge of image. Group sparse representation (GSR) is powerful for image restoration; it however ofte... Read More about Image restoration with group sparse representation and low‐rank group residual learning.

A directed graph convolutional neural network for edge-structured signals in link-fault detection (2021)
Journal Article
Kenning, M., Deng, J., Edwards, M., & Xie, X. (2022). A directed graph convolutional neural network for edge-structured signals in link-fault detection. Pattern Recognition Letters, 153, 100-106. https://doi.org/10.1016/j.patrec.2021.12.003

The growing interest in graph deep learning has led to a surge of research focusing on learning various characteristics of graph-structured data. Directed graphs have generally been treated as incidental to definitions on the more general class of un... Read More about A directed graph convolutional neural network for edge-structured signals in link-fault detection.