Výsledky vyhledávání - "Wang, Kuncan"

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Single-cell Curriculum Learning-based Deep Graph Embedding Clustering

Autor: Li, Huifa, Fu, Jie, Ling, Xinpeng, Sun, Zhiyu, Wang, Kuncan, Chen, Zhili

The swift advancement of single-cell RNA sequencing (scRNA-seq) technologies enables the investigation of cellular-level tissue heterogeneity. Cell annotation significantly contributes to the extensive downstream analysis of scRNA-seq data. However,

Externí odkaz: http://arxiv.org/abs/2408.10511

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FedFDP: Fairness-Aware Federated Learning with Differential Privacy

Autor: Ling, Xinpeng, Fu, Jie, Wang, Kuncan, Li, Huifa, Cheng, Tong, Chen, Zhili, Qian, Haifeng, Gong, Junqing

Federated learning (FL) is an emerging machine learning paradigm designed to address the challenge of data silos, attracting considerable attention. However, FL encounters persistent issues related to fairness and data privacy. To tackle these challe

Externí odkaz: http://arxiv.org/abs/2402.16028

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DPSUR: Accelerating Differentially Private Stochastic Gradient Descent Using Selective Update and Release

Autor: Fu, Jie, Ye, Qingqing, Hu, Haibo, Chen, Zhili, Wang, Lulu, Wang, Kuncan, Xun, Ran

Machine learning models are known to memorize private data to reduce their training loss, which can be inadvertently exploited by privacy attacks such as model inversion and membership inference. To protect against these attacks, differential privacy

Externí odkaz: http://arxiv.org/abs/2311.14056

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ALI-DPFL: Differentially Private Federated Learning with Adaptive Local Iterations

Autor: Ling, Xinpeng, Fu, Jie, Wang, Kuncan, Liu, Haitao, Chen, Zhili

Federated Learning (FL) is a distributed machine learning technique that allows model training among multiple devices or organizations by sharing training parameters instead of raw data. However, adversaries can still infer individual information thr

Externí odkaz: http://arxiv.org/abs/2308.10457

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