# ProtoFL: Unsupervised Federated Learning via Prototypical Distillation Source: [ProtoFL: Unsupervised Federated Learning via Prototypical Distillation]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:57:00.483Z Authors: Hansol Kim, Youngjun Kwak, Minyoung Jung, Jinho Shin, Youngsung Kim, Changick Kim Published: 2023-07-23T22:48:07.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()