93 citations · 134 across the 7 of their papers we have counts for
7 papers
Optimizing Performance of Federated Person Re-identification: Benchmarking and Analysis
Weiming Zhuang, Xin Gan, Yonggang Wen +1
The increasingly stringent data privacy regulations limit the development of person re-identification (ReID) because person ReID training requires centralizing an enormous amount o…
Federated Unsupervised Domain Adaptation for Face Recognition
Weiming Zhuang, Xin Gan, Yonggang Wen +3
Given labeled data in a source domain, unsupervised domain adaptation has been widely adopted to generalize models for unlabeled data in a target domain, whose data distributions a…
Collaborative Unsupervised Visual Representation Learning from Decentralized Data
Weiming Zhuang, Xin Gan, Yonggang Wen +2
Unsupervised representation learning has achieved outstanding performances using centralized data available on the Internet. However, the increasing awareness of privacy protection…
Towards Unsupervised Domain Adaptation for Deep Face Recognition under Privacy Constraints via Federated Learning
Weiming Zhuang, Xin Gan, Yonggang Wen +3
Unsupervised domain adaptation has been widely adopted to generalize models for unlabeled data in a target domain, given labeled data in a source domain, whose data distributions d…
EasyFL: A Low-code Federated Learning Platform For Dummies
Weiming Zhuang, Xin Gan, Yonggang Wen +1
Academia and industry have developed several platforms to support the popular privacy-preserving distributed learning method -- Federated Learning (FL). However, these platforms ar…
Robust optimization Design of a New Combined Median Barrier Based on Taguchi method and Grey Relational Analysis
Yupeng Huang, Song Yao, Peng Chen +3
Accidents that vehicles cross median and enter opposite lane happen frequently, and the existing median barrier has weak anti-collision strength. A new combined median barrier (NCM…
Performance Optimization for Federated Person Re-identification via Benchmark Analysis
Weiming Zhuang, Yonggang Wen, Xuesen Zhang +5
Federated learning is a privacy-preserving machine learning technique that learns a shared model across decentralized clients. It can alleviate privacy concerns of personal re-iden…