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Found 3 papers, 1 papers with code
Secure Federated Learning for Neuroimaging
Specifically, we investigate training neural models to classify Alzheimer's disease, and estimate Brain Age, from magnetic resonance imaging datasets distributed across multiple sites, including heterogeneous environments where sites have different amounts of data, statistical distributions, and computational capabilities.
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.
A Concurrency-Optimal List-Based Set
Designing an efficient concurrent data structure is an important challenge that is not easy to meet.
Distributed, Parallel, and Cluster Computing
