Search Results for author: Ating Yin

Found 2 papers, 1 papers with code

Federated Learning with New Knowledge: Fundamentals, Advances, and Futures

1 code implementation3 Feb 2024 Lixu Wang, Yang Zhao, Jiahua Dong, Ating Yin, Qinbin Li, Xiao Wang, Dusit Niyato, Qi Zhu

Federated Learning (FL) is a privacy-preserving distributed learning approach that is rapidly developing in an era where privacy protection is increasingly valued.

Federated Learning Privacy Preserving

Crucial Semantic Classifier-based Adversarial Learning for Unsupervised Domain Adaptation

no code implementations3 Feb 2023 Yumin Zhang, Yajun Gao, Hongliu Li, Ating Yin, Duzhen Zhang, Xiuyi Chen

Unsupervised Domain Adaptation (UDA), which aims to explore the transferrable features from a well-labeled source domain to a related unlabeled target domain, has been widely progressed.

Unsupervised Domain Adaptation

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