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

• 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: Article
• Language: English
• Published: 2024
• Subjects: Computer and Information Science; Artificial intelligence; Federated learning
• Online Access: https://hdl.handle.net/10356/179064
• Institution: Nanyang Technological University

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...