Efficient and privacy-preserving feature importance-based vertical federated learning

Efficient and privacy-preserving feature importance-based vertical federated learning

Vertical Federated Learning (VFL) enables multiple data owners, each holding a different subset of features about a largely overlapping set of data samples, to collaboratively train a global model. The quality of data owners’ local features affects the performance of the VFL model, which makes featu...

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Bibliographic Details
Main Authors: Li, Anran, Huang, Jiahui, Jia, Ju, Peng, Hongyi, Zhang, Lan, Tuan, Luu Anh, Yu, Han, Li, Xiang-Yang
Other Authors: College of Computing and Data Science
Format: Journal Article
Language:English
Published: 2024
Subjects:
Computer and Information Science
Artificial intelligence
Federated learning
Online Access:https://hdl.handle.net/10356/179064

Internet

https://hdl.handle.net/10356/179064

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