no code implementations • 2 Aug 2023 • Jiaojiao Zhang, Dominik Fay, Mikael Johansson
This paper proposes a locally differentially private federated learning algorithm for strongly convex but possibly nonsmooth problems that protects the gradients of each worker against an honest but curious server.
no code implementations • 5 Jul 2023 • Dominik Fay, Sebastian Mair, Jens Sjölund
We first consider the general case where an arbitrary personalized differentially private mechanism is subsampled with an arbitrary importance sampling distribution and show that the resulting mechanism also satisfies personalized differential privacy.
no code implementations • 10 Apr 2020 • Dominik Fay, Jens Sjölund, Tobias J. Oechtering
For this reason, we turn our attention to Private Aggregation of Teacher Ensembles (PATE), where all local models can be trained independently without inter-institutional communication.
3 code implementations • 20 Sep 2018 • Mart Kartašev, Carlo Rapisarda, Dominik Fay
As Deep Neural Networks are becoming more popular, much of the attention is being devoted to Computer Vision problems that used to be solved with more traditional approaches.