631 citations · 2.6k across the 22 of their papers we have counts for
24 papers
Trustworthy Federated Label Distribution Learning under Annotation Quality Disparity
Junxiang Wu, Zhiqiang Kou, Hongwei Zeng +7
Label Distribution Learning (LDL) models supervision as an instance-wise probability distribution, enabling fine-grained learning under inherent ambiguity, but its success relies o…
FedHarmony: Harmonizing Heterogeneous Label Correlations in Federated Multi-Label Learning
Zhiqiang Kou, Junxiang Wu, Wenke Huang +8
Federated Multi-Label Learning is a distributed paradigm where multiple clients possess heterogeneous multi-label data and perform collaborative learning under privacy constraints…
Eliminating Label Leakage in Tree-Based Vertical Federated Learning
Hideaki Takahashi, Jingjing Liu, Yang Liu
Vertical federated learning (VFL) enables multiple parties with disjoint features of a common user set to train a machine learning model without sharing their private data. Tree-ba…
Mutual Information Regularization for Vertical Federated Learning
Tianyuan Zou, Yang Liu, Ya-Qin Zhang
Vertical Federated Learning (VFL) is widely utilized in real-world applications to enable collaborative learning while protecting data privacy and safety. However, previous works s…
Multimodal Federated Learning via Contrastive Representation Ensemble
Qiying Yu, Yang Liu, Yimu Wang +2
With the increasing amount of multimedia data on modern mobile systems and IoT infrastructures, harnessing these rich multimodal data without breaching user privacy becomes a criti…
Vertical Federated Learning: Concepts, Advances and Challenges
Yang Liu, Yan Kang, Tianyuan Zou +6
Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with different features about the same set of users jointly train machine learning models w…