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Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients
[article]
2022
arXiv
pre-print
Supervised federated learning (FL) enables multiple clients to share the trained model without sharing their labeled data. However, potential clients might even be reluctant to label their own data, which could limit the applicability of FL in practice. In this paper, we show the possibility of unsupervised FL whose model is still a classifier for predicting class labels, if the class-prior probabilities are shifted while the class-conditional distributions are shared among the unlabeled data
arXiv:2204.03304v2
fatcat:5tbybbcsivdljkutjyj5r6vmai