Junxiang Wu — accepted AI-conference papers

2026

FedHarmony: Harmonizing Heterogeneous Label Correlations in Federated Multi-Label Learning

Zhiqiang Kou, Junxiang Wu, Wenke Huang, Wenwen He, Ming-Kun Xie, Changwei Wang +5

CVPR 2026

Federated Multi-Label Learning is a distributed paradigm where multiple clients possess heterogeneous multi-label data and perform collaborative learning under privacy constraints without sharing raw data. However, modeling label correlations under heterogeneous distributions remains challenging. Du

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2026

Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity

Junxiang Wu, Zhiqiang Kou, Hongwei Zeng, Wenke Huang, Biao Liu, Hanlin Gu +4

ICML 2026poster

Label Distribution Learning (LDL) models supervision as an instance-wise probability distribution, enabling fine-grained learning under inherent ambiguity, but its success relies on high-fidelity label distributions that are costly to obtain and thus often noisy. Motivated by privacy-sensitive appli…

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