Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: FedMVT: Semi-supervised Vertical Federated Learning with MultiView
  Training   Federated learning allows many parties to collaboratively build a model
without exposing data. Particularly, vertical federated learning (VFL) enables
parties to build a robust shared machine learning model based upon distributed
features about the same samples. However, VFL requires all parties to share a
sufficient amount of overlapping samples. In reality, the set of overlapping
samples may be small, leaving the majority of the non-overlapping data
unutilized. In this paper, we propose Federated Multi-View Training (FedMVT), a
semi-supervised learning approach that improves the performance of VFL with
limited overlapping samples. FedMVT estimates representations for missing
features and predicts pseudo-labels for unlabeled samples to expand training
set, and trains three classifiers jointly based upon different views of the
input to improve model's representation learning. FedMVT does not require
parties to share their original data and model parameters, thus preserving data
privacy. We conduct experiments on the NUS-WIDE and the CIFAR10. The
experimental results demonstrate that FedMVT significantly outperforms vanilla
VFL that only utilizes overlapping samples, and improves the performance of the
local model in the party that owns labels.

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The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
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The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
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The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
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The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
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The categories of this paper are: econ
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The categories of this paper are: math-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: funct-an
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The categories of this paper are: patt-sol
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The categories of this paper are: mtrl-th