Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Federated Self-Supervised Learning of Multi-Sensor Representations for
  Embedded Intelligence   Smartphones, wearables, and Internet of Things (IoT) devices produce a wealth
of data that cannot be accumulated in a centralized repository for learning
supervised models due to privacy, bandwidth limitations, and the prohibitive
cost of annotations. Federated learning provides a compelling framework for
learning models from decentralized data, but conventionally, it assumes the
availability of labeled samples, whereas on-device data are generally either
unlabeled or cannot be annotated readily through user interaction. To address
these issues, we propose a self-supervised approach termed
\textit{scalogram-signal correspondence learning} based on wavelet transform to
learn useful representations from unlabeled sensor inputs, such as
electroencephalography, blood volume pulse, accelerometer, and WiFi channel
state information. Our auxiliary task requires a deep temporal neural network
to determine if a given pair of a signal and its complementary viewpoint (i.e.,
a scalogram generated with a wavelet transform) align with each other or not
through optimizing a contrastive objective. We extensively assess the quality
of learned features with our multi-view strategy on diverse public datasets,
achieving strong performance in all domains. We demonstrate the effectiveness
of representations learned from an unlabeled input collection on downstream
tasks with training a linear classifier over pretrained network, usefulness in
low-data regime, transfer learning, and cross-validation. Our methodology
achieves competitive performance with fully-supervised networks, and it
outperforms pre-training with autoencoders in both central and federated
contexts. Notably, it improves the generalization in a semi-supervised setting
as it reduces the volume of labeled data required through leveraging
self-supervised learning.

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The categories of this paper are: physics
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The categories of this paper are: supr-con
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The categories of this paper are: hep-ex
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The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: cond-mat
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The categories of this paper are: adap-org
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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: q-bio
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-lat
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The categories of this paper are: gr-qc
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The categories of this paper are: q-alg