FEDERATED LEARNING OF BAYESIAN NEURAL NETWORKS

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.

Department dc:contributor.department

Computer Science (CS)

Grantor dc:publisher

Monterey, CA; Naval Postgraduate School

Year dc:date.issued

2023

Author dc:creator

• Loomis, Justin M.

Advisor dc:contributor.advisor

• Orescanin, Marko

Statement dc: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.

Handle dc:identifier.uri

https://hdl.handle.net/10945/72020

OAI identifier oai:identifier

oai:calhoun.nps.edu:10945/72020