评价联邦学习聚合算法:在人类活动识别中的应用

Sannara Ek, François Portet, P. Lalanda, Germán Vega
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引用次数: 29

摘要

普适计算促进了我们生活空间中连接电子设备的集成,以便通过适当的服务来帮助我们。最近有两项重大发展取得了显著进展:更好地利用雾资源和使用人工智能技术。具体来说,对工程应用中的机器学习方法的兴趣迅速增加。这种范式似乎很适合普遍的环境。然而,到目前为止,联邦学习已经应用于特定的服务,并且在很大程度上仍然是概念性的。它需要在部分位于雾中的普遍服务上进行广泛的测试。在本文中,我们提出了在智能手机上的人类活动识别领域进行的实验,以评估现有算法。
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Evaluation of federated learning aggregation algorithms: application to human activity recognition
Pervasive computing promotes the integration of connected electronic devices in our living spaces in order to assist us through appropriate services. Two major developments have gained significant momentum recently: a better use of fog resources and the use of AI techniques. Specifically, interest in machine learning approaches for engineering applications has increased rapidly. \ This paradigm seems to fit the pervasive environment well. However, federated learning has been applied so far to specific services and remains largely conceptual. It needs to be tested extensively on pervasive services partially located in the fog. In this paper, we present experiments performed in the domain of Human Activity Recognition on smartphones in order to evaluate existing algorithms.
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