Semi-supervised federated heterogeneous transfer learning

• Main Authors: Feng, Siwei, Li, Boyang, Yu, Han, Liu, Yang, Yang, Qiang
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Computer science and engineering; Federated Transfer Learning; Data Privacy Preservation
• Online Access: https://hdl.handle.net/10356/163377
• Institution: Nanyang Technological University

Federated learning (FL) is a privacy-preserving paradigm that collaboratively train machine learning models with distributed data stored in different silos without exposing sensitive information. Different from most existing FL approaches requiring data from different parties share either the same f...