1 code implementation • 27 Apr 2024 • Nannan Wu, Zhuo Kuang, Zengqiang Yan, Li Yu
In this study, we pioneer the identification and formulation of this new fairness challenge within the context of the imaging quality shift.
1 code implementation • 20 Dec 2023 • Nannan Wu, Zhaobin Sun, Zengqiang Yan, Li Yu
Specifically, noise estimation at each client is accomplished through the Gaussian mixture model and then incorporated into model aggregation in a layer-wise manner to up-weight high-quality clients.
2 code implementations • 9 May 2023 • Nannan Wu, Li Yu, Xuefeng Jiang, Kwang-Ting Cheng, Zengqiang Yan
Federated noisy label learning (FNLL) is emerging as a promising tool for privacy-preserving multi-source decentralized learning.
1 code implementation • 28 Jun 2022 • Nannan Wu, Li Yu, Xin Yang, Kwang-Ting Cheng, Zengqiang Yan
In this paper, we present a privacy-preserving FL method named FedIIC to combat class imbalance from two perspectives: feature learning and classifier learning.
no code implementations • 27 May 2022 • Nannan Wu, Ning Zhang, Wenjun Wang, Lixin Fan, Qiang Yang
The proposed algorithm FadMan is a vertical federated learning framework for public node aligned with many private nodes of different features, and is validated on two tasks correlated anomaly detection on multiple attributed networks and anomaly detection on an attributeless network using five real-world datasets.
no code implementations • 8 Jan 2021 • Nannan Wu, Qianwen Chao, Yanzhen Chen, Weiwei Xu, Chen Liu, Dinesh Manocha, Wenxin Sun, Yi Han, Xinran Yao, Xiaogang Jin
Given a query shape and pose of the virtual agent, we synthesize the resulting clothing deformation by blending the Taylor expansion results of nearby anchoring points.
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