Federated Bayesian Neural Networks for Passive Sonar

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.