FedX: Unsupervised Federated Learning with Cross Knowledge Distillation

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

ECCV 2022poster80 citations

FedX: Unsupervised Federated Learning with Cross Knowledge Distillation

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

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."

BibTeX

@inproceedings{eccv2022_fedxunsupervised,
  title = {FedX: Unsupervised Federated Learning with Cross Knowledge Distillation},
  author = {Sungwon Han and Sungwon Park and Fangzhao Wu and Sundong Kim and Chuhan Wu and Xing Xie and Meeyoung Cha},
  booktitle = {ECCV 2022},
  year = {2022}
}