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Found 9 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.
Federated Named Entity Recognition
We present an analysis of the performance of Federated Learning in a paradigmatic natural-language processing task: Named-Entity Recognition (NER).
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.
Membership Inference Attacks on Deep Regression Models for Neuroimaging
In particular, we show that it is possible to infer if a sample was used to train the model given only access to the model prediction (black-box) or access to the model itself (white-box) and some leaked samples from the training data distribution.
Scaling Neuroscience Research using Federated Learning
Federated Learning is a promising approach to learn a joint model over data silos.
Semi-Synchronous Federated Learning for Energy-Efficient Training and Accelerated Convergence in Cross-Silo Settings
There are situations where data relevant to machine learning problems are distributed across 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.
