Abstract
IoT devices have become increasingly popular in the last few years due to the potential of their sensors to feed data and provide insights into various applications in many fields. Today, such sensors are used in health care, environmental forecasting, and finance systems, to name a few. Predictive algorithms can leverage the temporal data provided by IoT sensors to enrich real-time applications to, for example, predict CO2 and temperature levels in a given region and provide public alerts. In this context, to find out the best solution for predicting time series generated from data collected by IoT devices, this work evaluates two machine learning approaches: federated learning and centralized learning. Federated learning implies training the algorithms in a distributed way across devices, while centralized learning takes data from devices into a server and focuses training on it. We performed experiments using Long Short Term Memory (LSTM) to predict the time series with federated and centralized strategies. The results show that the Federated Learning model predicts five time-steps of a time series with, on average, 78% less mean squared error and intakes 86% less communication load in the network than a Centralized solution.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
References
Amazon, E.: Amazon web services. http://aws.amazon.com/es/ec2/(November 2012), p. 39 (2015)
Bochie, K., Sammarco, M., Detyniecki, M., Campista, M.E.M.: Análise do aprendizado federado em redes móveis. In: Anais do XXXIX Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pp. 71–84. SBC (2021)
Imteaj, A., Thakker, U., Wang, S., Li, J., Amini, M.H.: Federated learning for resource-constrained IoT devices: panoramas and state-of-the-art. arXiv preprint arXiv:2002.10610 (2020)
Janssen, J.: Ventilation for acceptable indoor air quality. ASHRAE J. 31(10), 40–48 (1989)
Korstanje, J.: LSTM RNNs. In: Advanced Forecasting with Python, pp. 243–251. Springer (2021). https://doi.org/10.1007/978-1-4842-7150-6_18
Kourtellis, N., Katevas, K., Perino, D.: FLaaS: federated learning as a service. In: Proceedings of the 1st Workshop on Distributed Machine Learning, pp. 7–13 (2020)
Lim, W.Y.B., et al.: Federated learning in mobile edge networks: a comprehensive survey. IEEE Commun. Surv. Tutorials 22(3), 2031–2063 (2020)
Liu, Y., et al.: Deep anomaly detection for time-series data in industrial IoT: a communication-efficient on-device federated learning approach. IEEE Internet Things J. 8(8), 6348–6358 (2020)
McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282. PMLR (2017)
Merkel, D.: Docker: lightweight Linux containers for consistent development and deployment. Linux J. 2014(239), 2 (2014)
Navares, R., Aznarte, J.L.: Predicting air quality with deep learning LSTM: towards comprehensive models. Eco. Inform. 55, 101019 (2020)
Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d’Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems 32, pp. 8024–8035. Curran Associates, Inc. (2019). http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Sater, R.A., Hamza, A.B.: A federated learning approach to anomaly detection in smart buildings. ACM Trans. Internet Things 2(4), 1–23 (2021)
Tietz, M., Fan, T.J., Nouri, D., Bossan, B.: SKORCH: a scikit-learn compatible neural network library that wraps PyTorch, July 2017
Van Rossum, G., Drake, F.L.: Python 3 Reference Manual. CreateSpace, Scotts Valley, CA (2009)
Wu, Q., He, K., Chen, X.: Personalized federated learning for intelligent IoT applications: a cloud-edge based framework. IEEE Open J. Comput. Soc. 1, 35–44 (2020)
Yuan, B., Ge, S., Xing, W.: A federated learning framework for healthcare IoT devices. arXiv preprint arXiv:2005.05083 (2020)
Zhang, X., Wargocki, P., Lian, Z., Thyregod, C.: Effects of exposure to carbon dioxide and bioeffluents on perceived air quality, self-assessed acute health symptoms, and cognitive performance. Indoor Air 27(1), 47–64 (2017)
Acknowledgment
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.
The authors would like to thank The Ceará State Foundation for the Support of Scientific and Technological Development (FUNCAP) for the financial support (6945087/2019).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Furtado, L.S., Costa, L.F.d., Rocha, P.H.G., Rego, P.A.L. (2022). A Comparative Study of Federated Versus Centralized Learning for Time Series Prediction in IoT. In: Xavier-Junior, J.C., Rios, R.A. (eds) Intelligent Systems. BRACIS 2022. Lecture Notes in Computer Science(), vol 13654 . Springer, Cham. https://doi.org/10.1007/978-3-031-21689-3_22
Download citation
DOI: https://doi.org/10.1007/978-3-031-21689-3_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21688-6
Online ISBN: 978-3-031-21689-3
eBook Packages: Computer ScienceComputer Science (R0)