  Federated learning (FL) is a promising approach for enhancing data privacy
preservation, particularly for authentication systems. However, limited round
communications, scarce representation, and scalability pose significant
challenges to its deployment, hindering its full potential. In this paper, we
propose 'ProtoFL', Prototypical Representation Distillation based unsupervised
Federated Learning to enhance the representation power of a global model and
reduce round communication costs. Additionally, we introduce a local one-class
classifier based on normalizing flows to improve performance with limited data.
Our study represents the first investigation of using FL to improve one-class
classification performance. We conduct extensive experiments on five widely
used benchmarks, namely MNIST, CIFAR-10, CIFAR-100, ImageNet-30, and
Keystroke-Dynamics, to demonstrate the superior performance of our proposed
framework over previous methods in the literature.
