Paper 2022/784
Fully Privacy-Preserving Federated Representation Learning via Secure Embedding Aggregation
Abstract
We consider a federated representation learning framework, where with the assistance of a central server, a group of
Metadata
- Available format(s)
-
PDF
- Category
- Cryptographic protocols
- Publication info
- Preprint.
- Keywords
- federated learning federated representation learning private set union secure embedding aggregation
- Contact author(s)
-
jathon tang @ gmail com
songzeli @ ust hk - History
- 2022-06-20: approved
- 2022-06-18: received
- See all versions
- Short URL
- https://ia.cr/2022/784
- License
-
CC BY-NC-ND
BibTeX
@misc{cryptoeprint:2022/784, author = {Jiaxiang Tang and Jinbao Zhu and Songze Li and Kai Zhang and Lichao Sun}, title = {Fully Privacy-Preserving Federated Representation Learning via Secure Embedding Aggregation}, howpublished = {Cryptology {ePrint} Archive, Paper 2022/784}, year = {2022}, url = {https://eprint.iacr.org/2022/784} }