Federated f-Differential Privacy

Qinqing Zheng, Shuxiao Chen, Qi Long, Weijie Su
Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, PMLR 130:2251-2259, 2021.

Abstract

Federated learning (FL) is a training paradigm where the clients collaboratively learn models by repeatedly sharing information without compromising much on the privacy of their local sensitive data. In this paper, we introduce \emph{federated $f$-differential privacy}, a new notion specifically tailored to the federated setting, based on the framework of Gaussian differential privacy. Federated $f$-differential privacy operates on \emph{record level}: it provides the privacy guarantee on each individual record of one client’s data against adversaries. We then propose a generic private federated learning framework \fedsync that accommodates a large family of state-of-the-art FL algorithms, which provably achieves {federated $f$-differential privacy}. Finally, we empirically demonstrate the trade-off between privacy guarantee and prediction performance for models trained by \fedsync in computer vision tasks.

Cite this Paper

BibTeX
@InProceedings{pmlr-v130-zheng21a, title = { Federated f-Differential Privacy }, author = {Zheng, Qinqing and Chen, Shuxiao and Long, Qi and Su, Weijie}, booktitle = {Proceedings of The 24th International Conference on Artificial Intelligence and Statistics}, pages = {2251--2259}, year = {2021}, editor = {Banerjee, Arindam and Fukumizu, Kenji}, volume = {130}, series = {Proceedings of Machine Learning Research}, month = {13--15 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v130/zheng21a/zheng21a.pdf}, url = {https://proceedings.mlr.press/v130/zheng21a.html}, abstract = { Federated learning (FL) is a training paradigm where the clients collaboratively learn models by repeatedly sharing information without compromising much on the privacy of their local sensitive data. In this paper, we introduce \emph{federated $f$-differential privacy}, a new notion specifically tailored to the federated setting, based on the framework of Gaussian differential privacy. Federated $f$-differential privacy operates on \emph{record level}: it provides the privacy guarantee on each individual record of one client’s data against adversaries. We then propose a generic private federated learning framework \fedsync that accommodates a large family of state-of-the-art FL algorithms, which provably achieves {federated $f$-differential privacy}. Finally, we empirically demonstrate the trade-off between privacy guarantee and prediction performance for models trained by \fedsync in computer vision tasks. } }
Endnote
%0 Conference Paper %T Federated f-Differential Privacy %A Qinqing Zheng %A Shuxiao Chen %A Qi Long %A Weijie Su %B Proceedings of The 24th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2021 %E Arindam Banerjee %E Kenji Fukumizu %F pmlr-v130-zheng21a %I PMLR %P 2251--2259 %U https://proceedings.mlr.press/v130/zheng21a.html %V 130 %X Federated learning (FL) is a training paradigm where the clients collaboratively learn models by repeatedly sharing information without compromising much on the privacy of their local sensitive data. In this paper, we introduce \emph{federated $f$-differential privacy}, a new notion specifically tailored to the federated setting, based on the framework of Gaussian differential privacy. Federated $f$-differential privacy operates on \emph{record level}: it provides the privacy guarantee on each individual record of one client’s data against adversaries. We then propose a generic private federated learning framework \fedsync that accommodates a large family of state-of-the-art FL algorithms, which provably achieves {federated $f$-differential privacy}. Finally, we empirically demonstrate the trade-off between privacy guarantee and prediction performance for models trained by \fedsync in computer vision tasks.
APA
Zheng, Q., Chen, S., Long, Q. & Su, W.. (2021). Federated f-Differential Privacy . Proceedings of The 24th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 130:2251-2259 Available from https://proceedings.mlr.press/v130/zheng21a.html.

Related Material