FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation
Abstract
Vertical federated learning (VFL) allows an active party with labeled data to leverage auxiliary features from the passive parties to improve model performance. Concerns about the private feature and label leakage in both the training and inference phases of VFL have drawn wide research attention. In this paper, we propose a general privacy-preserving vertical federated deep learning framework called FedPass, which leverages adaptive obfuscation to protect the feature and label simultaneously. Strong privacy-preserving capabilities about private features and labels are theoretically proved (in Theorems 1 and 2). Extensive experimental results with different datasets and network architectures also justify the superiority of FedPass against existing methods in light of its near-optimal trade-off between privacy and model performance.
Cite
Text
Gu et al. "FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation." International Joint Conference on Artificial Intelligence, 2023. doi:10.24963/IJCAI.2023/418Markdown
[Gu et al. "FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation." International Joint Conference on Artificial Intelligence, 2023.](https://mlanthology.org/ijcai/2023/gu2023ijcai-fedpass/) doi:10.24963/IJCAI.2023/418BibTeX
@inproceedings{gu2023ijcai-fedpass,
title = {{FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation}},
author = {Gu, Hanlin and Luo, Jiahuan and Kang, Yan and Fan, Lixin and Yang, Qiang},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2023},
pages = {3759-3767},
doi = {10.24963/IJCAI.2023/418},
url = {https://mlanthology.org/ijcai/2023/gu2023ijcai-fedpass/}
}