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Found 5 papers, 1 papers with code
Hawk: Accurate and Fast Privacy-Preserving Machine Learning Using Secure Lookup Table Computation
In this work, we design and implement new privacy-preserving machine learning protocols for logistic regression and neural network models.
Secure & Private Federated Neuroimaging
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.
Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption
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.
Exacerbating Algorithmic Bias through Fairness Attacks
Algorithmic fairness has attracted significant attention in recent years, with many quantitative measures suggested for characterizing the fairness of different machine learning algorithms.
Competitive numerical analysis for stochastic HIV/AIDS epidemic model in a two-sex population
This study is an attempt to explain a reliable numerical analysis of a stochastic HIV/AIDS model in a two-sex population considering counselling and antiretroviral therapy (ART).
