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.

翻译：联邦学习是增强数据隐私保护的一种有前景的方法，尤其在认证系统中。然而，有限的通信轮次、稀缺的表示能力及可扩展性对其部署构成重大挑战，限制了其全部潜力。本文提出"ProtoFL"——基于原型表示蒸馏的无监督联邦学习，旨在增强全局模型的表示能力并降低通信轮次成本。此外，我们引入基于归一化流的局部单类分类器，以在数据有限的情况下提升性能。本研究首次探索利用联邦学习改进单类分类性能。我们在五个广泛使用的基准数据集（即MNIST、CIFAR-10、CIFAR-100、ImageNet-30和Keystroke-Dynamics）上进行了大量实验，证明所提框架相较于文献中先前方法具有优越性能。