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arXiv · 2207.09158

FedX: Unsupervised Federated Learning with Cross Knowledge Distillation

Sungwon Han·Sungwon Park·Fangzhao Wu·Sundong Kim·Chuhan Wu·Xing Xie·Meeyoung Cha
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Abstract

This paper presents FedX, an unsupervised federated learning framework. Our model learns unbiased representation from decentralized and heterogeneous local data. It employs a two-sided knowledge distillation with contrastive learning as a core component, allowing the federated system to function without requiring clients to share any data features. Furthermore, its adaptable architecture can be used as an add-on module for existing unsupervised algorithms in federated settings. Experiments show that our model improves performance significantly (1.58--5.52pp) on five unsupervised algorithms.

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Sungwon Han, Sungwon Park, Fangzhao Wu, Sundong Kim, Chuhan Wu, Xing Xie, Meeyoung Cha. 2022-07-19. FedX: Unsupervised Federated Learning with Cross Knowledge Distillation. https://arxiv.org/abs/2207.09158

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