Using Federated Learning to Achieve Proactive Context-Aware IoT Environments

Authors

  • Rubén Rentero-Trejo University of Extremadura, Spain
  • Daniel Flores-Martín University of Extremadura, Spain
  • Jaime Galán-Jiménez University of Extremadura, Spain
  • José García-Alonso University of Extremadura, Spain
  • Juan Manuel Murillo University of Extremadura, Spain
  • Javier Berrocal University of Extremadura, Spain

DOI:

https://doi.org/10.13052/jwe1540-9589.2113

Keywords:

Federated learning, mobile devices, context-aware, IoT

Abstract

The Internet of Things (IoT) is more present in our daily lives than ever before, turning everyday physical objects into smart devices. However, these devices often need excessive human interaction before reaching their best performance, making them time-consuming and reducing their usability. Nowadays, Artificial Intelligence (AI) techniques are being used to process data and to find ways to automate different behaviours. However, achieving learning models capable of handling any situation is a challenging task, worsened by time training restrictions. This paper proposes a Federated Learning solution to manage different IoT environments and provide accurate predictions, based on the user’s preferences. To improve the coexistence between devices and users, this approach makes use of other users’ previous behaviours in similar environments, and proposes predictions for newcomers to the federation. Also, for existing participants, it provides a closer personalization, immediate availability and prevents most manual interactions. The approach has been tested with synthetic and real data and identifies the actions to be performed with 94% accuracy on regular users.

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Author Biographies

Rubén Rentero-Trejo, University of Extremadura, Spain

Rubén Rentero-Trejo. Received the Bachelor’s degree in software engineering from the University of Extremadura in 2019, and the MSc in Computer Science Engineering in 2021. His main research interests include IoT, mobile computing and Deep Learning.

Daniel Flores-Martín, University of Extremadura, Spain

Daniel Flores-Martín. Is a Ph.D student at the University of Extremadura (Spain). His research interests are mobile computing, context-awareness, pervasive systems, crowd sensing and Internet of Things.

Jaime Galán-Jiménez, University of Extremadura, Spain

Jaime Galán-Jiménez. Received the Ph.D. in computer science and communications from the University of Extremadura in 2014. His main research interests are Software-Defined Networks, 5G network planning and design, and mobile ad-hoc networks.

José García-Alonso, University of Extremadura, Spain

José García-Alonso (IEEE Member) is an Associate Professor at the University of Extremadura. His research interests include software engineering, mobile computing, pervasive computing, eHealth, gerontechnology.

Juan Manuel Murillo, University of Extremadura, Spain

Juan Manuel Murillo (IEEE Member) is a full professor at the University of Extremadura. His research interests include software architectures, mobile computing, and cloud computing.

Javier Berrocal, University of Extremadura, Spain

Javier Berrocal (IEEE Member) is an Associate Professor at the University of Extremadura. His main research interests are software architectures, mobile computing and edge and fog computing.

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Published

2021-11-28

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