Search Results for author: Changting Lin

Found 2 papers, 1 papers with code

A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective

no code implementations6 Feb 2024 Lei Yu, Meng Han, Yiming Li, Changting Lin, Yao Zhang, Mingyang Zhang, Yan Liu, Haiqin Weng, Yuseok Jeon, Ka-Ho Chow, Stacy Patterson

Vertical Federated Learning (VFL) is a federated learning paradigm where multiple participants, who share the same set of samples but hold different features, jointly train machine learning models.

Vertical Federated Learning

Rethinking the Defense Against Free-rider Attack From the Perspective of Model Weight Evolving Frequency

1 code implementation11 Jun 2022 Jinyin Chen, Mingjun Li, Tao Liu, Haibin Zheng, Yao Cheng, Changting Lin

To address these challenges, we reconsider the defense from a novel perspective, i. e., model weight evolving frequency. Empirically, we gain a novel insight that during the FL's training, the model weight evolving frequency of free-riders and that of benign clients are significantly different.

Federated Learning Privacy Preserving

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