By averaging classifier weights per label across clients and tuning the central model on unlabeled data, federated learning can handle private, heterogeneous client label sets at accuracy close to the public-label setting.
Federated semi-supervised learning with inter-client consistency & disjoint learning
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Federated Learning with Heterogeneous and Private Label Sets
By averaging classifier weights per label across clients and tuning the central model on unlabeled data, federated learning can handle private, heterogeneous client label sets at accuracy close to the public-label setting.