FEDERATED LEARNING OF BAYESIAN NEURAL NETWORKS
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Authors
Loomis, Justin M.
Subjects
Bayesian neural networks
federated learning
independently and identically distributed
IID
federated learning
independently and identically distributed
IID
Advisors
Orescanin, Marko
Date of Issue
2023-03
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
Although federated learning and Bayesian neural networks have been researched, there are few implementations of the federated learning of Bayesian networks. In this thesis, a federated learning training environment for Bayesian neural networks using a public code base, Flower, is developed. With it is the exploration of state-of-the-art architecture, residual networks, and Bayesian versions of it. These architectures are then tested with independently and identically distributed (IID) datasets and non-IID datasets derived from the Dirichlet distribution. Results show that the MC Dropout version of Bayesian neural networks can achieve state-of-the-art results—91% accuracy—for IID partitions of the CIFAR10 dataset through federated learning. When the partitions are non-IID, federated learning through inverse variance aggregation of probabilistic weights does as well as its deterministic counterpart, with roughly 83% accuracy. This shows that Bayesian neural networks can be federated and achieve state-of-the-art results as well.
Type
Thesis
Description
Series/Report No
Department
Computer Science (CS)
Organization
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NPS Report Number
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Citation
Distribution Statement
Approved for public release. Distribution is unlimited.
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.