4 citations · 5 across the 4 of their papers we have counts for
5 papers
Dynamic Privacy Allocation for Locally Differentially Private Federated Learning with Composite Objectives
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 aga…
Personalized Privacy Amplification via Importance Sampling
Dominik Fay, Sebastian Mair, Jens Sjölund
For scalable machine learning on large data sets, subsampling a representative subset is a common approach for efficient model training. This is often achieved through importance s…
Decentralized Differentially Private Segmentation with PATE
Dominik Fay, Jens Sjölund, Tobias J. Oechtering
When it comes to preserving privacy in medical machine learning, two important considerations are (1) keeping data local to the institution and (2) avoiding inference of sensitive…
Privacy of Real-Time Pricing in Smart Grid
Mahrokh GhoddousiBoroujeni, Dominik Fay, Christos Dimitrakakis +1
Installing smart meters to publish real-time electricity rates has been controversial while it might lead to privacy concerns. Dispatched rates include fine-grained data on aggrega…
Implementing Adaptive Separable Convolution for Video Frame Interpolation
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. Video…