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Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of Models
[article]
2021
arXiv
pre-print
Federated learning (FL) is a promising way to use the computing power of mobile devices while maintaining the privacy of users. Current work in FL, however, makes the unrealistic assumption that the users have ground-truth labels on their devices, while also assuming that the server has neither data nor labels. In this work, we consider the more realistic scenario where the users have only unlabeled data, while the server has some labeled data, and where the amount of labeled data is smaller
arXiv:2008.11364v2
fatcat:ixy7htug7favtl6pjhogualbxa