Federated and continual learning for classification tasks in a society of devices
Today we live in a context in which devices are increasingly interconnected and sensorized and are almost ubiquitous. Deep learning has become in recent years a popular way to extract knowledge from the huge amount of data that these devices are able to collect. Nevertheless, centralized state-of-th...
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Main Authors | , , , , |
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Format | Journal Article |
Language | English |
Published |
12.06.2020
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Subjects | |
Online Access | Get full text |
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Summary: | Today we live in a context in which devices are increasingly interconnected
and sensorized and are almost ubiquitous. Deep learning has become in recent
years a popular way to extract knowledge from the huge amount of data that
these devices are able to collect. Nevertheless, centralized state-of-the-art
learning methods have a number of drawbacks when facing real distributed
problems, in which the available information is usually private, partial,
biased and evolving over time. Federated learning is a popular framework that
allows multiple distributed devices to train models remotely, collaboratively,
and preserving data privacy. However, the current proposals in federated
learning focus on deep architectures that in many cases are not feasible to
implement in non-dedicated devices such as smartphones. Also, little research
has been done regarding the scenario where data distribution changes over time
in unforeseen ways, causing what is known as concept drift. Therefore, in this
work we want to present Light Federated and Continual Consensus (LFedCon2), a
new federated and continual architecture that uses light, traditional learners.
Our method allows powerless devices (such as smartphones or robots) to learn in
real time, locally, continuously, autonomously and from users, but also
improving models globally, in the cloud, combining what is learned locally, in
the devices. In order to test our proposal, we have applied it in a
heterogeneous community of smartphone users to solve the problem of walking
recognition. The results show the advantages that LFedCon2 provides with
respect to other state-of-the-art methods. |
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DOI: | 10.48550/arxiv.2006.07129 |