43 citations · 44 across the 3 of their papers we have counts for
4 papers
Privacy Amplification via Random Participation in Federated Learning
Burak Hasircioglu, Deniz Gunduz
Running a randomized algorithm on a subsampled dataset instead of the entire dataset amplifies differential privacy guarantees. In this work, in a federated setting, we consider ra…
Over-the-Air Ensemble Inference with Model Privacy
Selim F. Yilmaz, Burak Hasircioglu, Deniz Gunduz
We consider distributed inference at the wireless edge, where multiple clients with an ensemble of models, each trained independently on a local dataset, are queried in parallel to…
Speeding Up Private Distributed Matrix Multiplication via Bivariate Polynomial Codes
Burak Hasircioglu, Jesus Gomez-Vilardebo, Deniz Gunduz
We consider the problem of private distributed matrix multiplication under limited resources. Coded computation has been shown to be an effective solution in distributed matrix mul…
Dopamine: Differentially Private Federated Learning on Medical Data
Mohammad Malekzadeh, Burak Hasircioglu, Nitish Mital +3
While rich medical datasets are hosted in hospitals distributed across the world, concerns on patients' privacy is a barrier against using such data to train deep neural networks (…