Privacy-Preserving Learning Models for Communication: A tutorial on Advanced Split Learning
- Authors
- Tran, N.-P.; Dao, N.-N.; Nguyen, T.-V.; Cho, Sungrae
- Issue Date
- Oct-2022
- Publisher
- IEEE Computer Society
- Keywords
- Distributed Collaborative Machine Learning; Federated Learning; privacy-preserving; Split Learning
- Citation
- International Conference on ICT Convergence, v.2022-October, pp 1059 - 1064
- Pages
- 6
- Journal Title
- International Conference on ICT Convergence
- Volume
- 2022-October
- Start Page
- 1059
- End Page
- 1064
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/59780
- DOI
- 10.1109/ICTC55196.2022.9952628
- ISSN
- 2162-1233
- Abstract
- Along with the development of the internet, there is an explosion of data that is assembled from diverse sources such as mobile phones or sensors with unprecedented volumes. Big data paves the way for devising state-of-the-art Machine Learning models, which are employed for various tasks such as predicting or analysis, and bring massive impacts on people's daily lives. However, traditional learning architectures pose risks of privacy leakage. In deal with this problem, Split Learning was introduced. Split Learning is an emerging Distributed Collaborative Machine Learning approach with privacy-preserving nature. In this work, we first introduce the fundamentals of Split Learning and then describe major challenges of current learning models. We then depict modern Split Learning and their potential applications in variety fields. © 2022 IEEE.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Software > School of Computer Science and Engineering > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.