Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SemiFL: Communication Efficient Semi-Supervised Federated Learning with
  Unlabeled Clients   Federated Learning (FL) allows training machine learning models by using the
computation and private data resources of many distributed clients such as
smartphones and IoT devices. Most existing works on FL assume the clients have
ground-truth labels. However, in many practical scenarios, clients do not have
task-specific labels, e.g., due to a lack of expertise. This work considers a
server that hosts a labeled dataset and wishes to leverage clients with
unlabeled data for supervised learning. We propose a new FL framework referred
to as SemiFL to address Semi-Supervised Federated Learning (SSFL). In SemiFL,
clients have completely unlabeled data, while the server has a small amount of
labeled data. SemiFL is communication efficient since it allows to train
client-side unsupervised data for multiple local epochs. We demonstrate several
strategies in SemiFL to enhance efficiency and prediction, and develop
intuitions of why they work. In particular, we provide a theoretical analysis
of the use of strong data augmentation for semi-supervised learning, which can
be interesting in its own right. Extensive empirical evaluations demonstrate
that SemiFL can significantly improve the performance of a labeled server with
unlabeled clients. Moreover, SemiFL can outperform many existing SSFL methods,
and perform competitively with the state-of-the-art FL and centralized SSL
results. For instance, in the standard communication efficient scenarios,
SemiFL gives 93% accuracy on the CIFAR10 dataset with only 4000 labeled samples
at the server. Such accuracy is 2% away from the result trained from 50000
fully labeled data, and improves 30% upon existing SSFL methods.

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The categories of this paper are: alg-geom
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The categories of this paper are: solv-int
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The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: funct-an
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The categories of this paper are: econ
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The categories of this paper are: nucl-ex
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The categories of this paper are: ao-sci
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The categories of this paper are: astro-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: cond-mat
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The categories of this paper are: math
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The categories of this paper are: physics
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The categories of this paper are: plasm-ph
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The categories of this paper are: nlin
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The categories of this paper are: q-fin