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Found 10 papers, 0 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.
Performance Weighting for Robust Federated Learning Against Corrupted Sources
Here, we focus on the latter, the susceptibility of federated learning to various data corruption attacks.
Federated Progressive Sparsification (Purge, Merge, Tune)+
Second, the models are incrementally constrained to a smaller set of parameters, which facilitates alignment/merging of the local models and improved learning performance at high sparsification rates.
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.
Scaling Neuroscience Research using Federated Learning
Federated Learning is a promising approach to learn a joint model over data silos.
Semi-Synchronous Federated Learning
There are situations where data relevant to a machine learning problem are distributed among multiple locations that cannot share the data due to regulatory, competitiveness, or privacy reasons.
Accelerating Federated Learning in Heterogeneous Data and Computational Environments
There are situations where data relevant to a machine learning problem are distributed among multiple locations that cannot share the data due to regulatory, competitiveness, or privacy reasons.
NSEEN: Neural Semantic Embedding for Entity Normalization
An important task in this process is entity normalization, which consists of mapping noisy entity mentions in text to canonical entities in well-known reference sets.
Learning the Semantics of Structured Data Sources
This model represents the semantics of the new source in terms of the concepts and relationships defined by the domain ontology.
