Federated Bayesian Deep Learning: The Application of Statistical Aggregation Methods to Bayesian Models

Federated Bayesian Deep Learning: The Application of Statistical Aggregation Methods to Bayesian Models

Federated learning (FL) is an approach to training machine learning models that takes advantage of multiple distributed datasets while maintaining data privacy and reducing communication costs associated with sharing local datasets. Aggregation strategies have been developed to pool or fuse the weig...

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Bibliographic Details
Main Authors: John Fischer, Marko Orescanin, Justin Loomis, Patrick Mcclure
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Bayesian deep learning
federated learning
Monte Carlo dropout
uncertainty decomposition
uncertainty quantification
variational inference
Online Access:https://ieeexplore.ieee.org/document/10781394/
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