activity
20202024
most citedDopamine: Differentially Private Federated Learning on Medical Data

43 citations · 69 across the 7 of their papers we have counts for

collaborators

7 papers

cs.LG2024★ 8 cited

Private Collaborative Edge Inference via Over-the-Air Computation

Selim F. Yilmaz, Burak Hasircioglu, Li Qiao +1

We consider collaborative inference at the wireless edge, where each client's model is trained independently on its local dataset. Clients are queried in parallel to make an accura…

cs.LG2023

Communication Efficient Private Federated Learning Using Dithering

Burak Hasircioglu, Deniz Gunduz

The task of preserving privacy while ensuring efficient communication is a fundamental challenge in federated learning. In this work, we tackle this challenge in the trusted aggreg…

cs.LG2022★ 1 cited

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…

cs.LG2022

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…

cs.IT2021

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…

cs.LG2021★ 43 cited

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 (…

cs.IT2020★ 17 cited

Bivariate Polynomial Coding for Efficient Distributed Matrix Multiplication

Burak Hasircioglu, Jesus Gomez-Vilardebo, Deniz Gunduz

Coded computing is an effective technique to mitigate "stragglers" in large-scale and distributed matrix multiplication. In particular, univariate polynomial codes have been shown…