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Outputs (3)

An Element-Wise Weights Aggregation Method for Federated Learning (2023)
Presentation / Conference Contribution
Hu, Y., Ren, H., Hu, C., Deng, J., & Xie, X. (2023, December). An Element-Wise Weights Aggregation Method for Federated Learning. Presented at 2023 IEEE International Conference on Data Mining Workshops (ICDMW), Shanghai, China

Federated learning (FL) is a powerful Machine Learning (ML) paradigm that enables distributed clients to collaboratively learn a shared global model while keeping the data on the original device, thereby preserving privacy. A central challenge in FL... Read More about An Element-Wise Weights Aggregation Method for Federated Learning.

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. (2024). Image restoration with group sparse representation and low‐rank group residual learning. IET Image Processing, 18(3), 741-760. 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.