Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Federated Learning with Positive and Unlabeled Data   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.

22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
16
The categories of this paper are: stat
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph