Search Results for author: Hamza Saleem

Found 3 papers, 0 papers with code

Hawk: Accurate and Fast Privacy-Preserving Machine Learning Using Secure Lookup Table Computation

no code implementations26 Mar 2024 Hamza Saleem, Amir Ziashahabi, Muhammad Naveed, Salman Avestimehr

In this work, we design and implement new privacy-preserving machine learning protocols for logistic regression and neural network models.

Computational Efficiency Privacy Preserving +1

Secure & Private Federated Neuroimaging

no code implementations11 May 2022 Dimitris Stripelis, Umang Gupta, Hamza Saleem, Nikhil Dhinagar, Tanmay Ghai, Rafael Chrysovalantis Anastasiou, Armaghan Asghar, Greg Ver Steeg, Srivatsan Ravi, Muhammad Naveed, Paul M. Thompson, Jose Luis Ambite

Each site trains the neural network over its private data for some time, then shares the neural network parameters (i. e., weights, gradients) with a Federation Controller, which in turn aggregates the local models, sends the resulting community model back to each site, and the process repeats.

Federated Learning

Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption

no code implementations7 Aug 2021 Dimitris Stripelis, Hamza Saleem, Tanmay Ghai, Nikhil Dhinagar, Umang Gupta, Chrysovalantis Anastasiou, Greg Ver Steeg, Srivatsan Ravi, Muhammad Naveed, Paul M. Thompson, Jose Luis Ambite

Federated learning (FL) enables distributed computation of machine learning models over various disparate, remote data sources, without requiring to transfer any individual data to a centralized location.

Benchmarking Federated Learning

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