Privacy-enhanced knowledge transfer with collaborative split learning over teacher ensembles

• المؤلفون الرئيسيون: Liu, Ziyao, Guo, Jiale, Yang, Mengmeng, Yang, Wenzhuo, Fan, Jiani, Lam, Kwok-Yan
• مؤلفون آخرون: School of Computer Science and Engineering
• التنسيق: Conference or Workshop Item
• اللغة: English
• منشور في: 2023
• الموضوعات: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence; Privacy-Preservation; Knowledge Transfer
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/172524

Knowledge Transfer has received much attention for its ability to transfer knowledge, rather than data, from one application task to another. In order to comply with the stringent data privacy regulations, privacy-preserving knowledge transfer is highly desirable. The Private Aggregation of Teacher...