SensFL: Privacy-Preserving Vertical Federated Learning with Sensitive Regularization
In the realm of Intelligent Railway Transportation Systems, effective multi-party collaboration is crucial due to concerns over privacy and data silos. Vertical Federated Learning (VFL) has emerged as a promising approach to ... | Find, read and cite all the research you need on Tech Science Press
Authors
- Xiangrui Xu (ORCID: https://orcid.org/0000-0003-3759-4706)
- Jiao Dai (ORCID: https://orcid.org/0000-0003-3559-8009)
- Zhichen Liu (ORCID: https://orcid.org/0000-0002-1387-2516)
- Cheng Zhang (ORCID: https://orcid.org/0000-0001-8932-0585)
- Baigen Cai (ORCID: https://orcid.org/0000-0002-6440-005X)
- Fuqiang Hu
- Wei Wang (ORCID: https://orcid.org/0000-0002-5750-6980)
Publication Details
- Journal
- Computer Modeling in Engineering & Sciences
- Published
- 2024-11-20
- DOI
- https://doi.org/10.32604/cmes.2024.055596
- Citations
- 3
- Primary Topic
- Privacy-Preserving Technologies in Data
- Type
- article
- Field-Weighted Citation Impact
- 1.04