Collaborative and continual learning for classification tasks in a society of devices
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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, state-of-the-art learning methods have a number of drawbacks when facing real distributed problems, in which the available information is usually partial, biased and evolving over time. Moreover, if there is something that characterizes this society of devices is its high heterogeneity and dynamism, both in terms of users and the hardware itself. Therefore, against the tendency to centralize learning, in this work we want to present a new paradigm of learning in society, where devices get certain prominence back, having 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. Hence, learning is carried out in a cyclical process of global consensus and local adaptation that can be repeated indefinitely over time. In this work we present a first architecture for this paradigm, which we call "glocal" learning. 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 "glocal" learning provides with respect to other state-of-the-art methods.
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