Federated Learning with Positive and Unlabeled Data
- URL: http://arxiv.org/abs/2106.10904v1
- Date: Mon, 21 Jun 2021 08:05:51 GMT
- Title: Federated Learning with Positive and Unlabeled Data
- Authors: Xinyang Lin, Hanting Chen, Yixing Xu, Chao Xu, Xiaolin Gui, Yiping
Deng, Yunhe Wang
- Abstract summary: We study the problem of learning from positive and unlabeled (PU) data in the federated setting.
We propose a novel framework, namely Federated learning with Positive and Unlabeled data (FedPU)
We show that the FedPU can achieve much better performance than conventional learning methods which can only use positive data.
- Score: 39.4214951077286
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We study the problem of learning from positive and unlabeled (PU) data in the
federated setting, where each client only labels a little part of their dataset
due to the limitation of resources and time. Different from the settings in
traditional PU learning where the negative class consists of a single class,
the negative samples which cannot be identified by a client in the federated
setting may come from multiple classes which are unknown to the client.
Therefore, existing PU learning methods can be hardly applied in this
situation. To address this problem, we propose a novel framework, namely
Federated learning with Positive and Unlabeled data (FedPU), to minimize the
expected risk of multiple negative classes by leveraging the labeled data in
other clients. We theoretically prove that the proposed FedPU can achieve a
generalization bound which is no worse than $C\sqrt{C}$ times (where $C$
denotes the number of classes) of the fully-supervised model. Empirical
experiments show that the FedPU can achieve much better performance than
conventional learning methods which can only use positive data.
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