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Article

Open-Source Federated Learning Frameworks for IoT: A Comparative Review and Analysis

1
Faculty of Computer Science and Technology, Saint Petersburg Electrotechnical University “LETI”, Saint Petersburg 197376, Russia
2
Smartilizer Rus LLC, Saint Petersburg 197376, Russia
3
AI Sweden, Zenseact AB, Smartilizer Scandinavia AB, 417 56 Goteborg, Sweden
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(1), 167; https://doi.org/10.3390/s21010167
Received: 24 November 2020 / Revised: 20 December 2020 / Accepted: 24 December 2020 / Published: 29 December 2020
(This article belongs to the Special Issue Evolution of Distributed Computing in Sensor Systems)
The rapid development of Internet of Things (IoT) systems has led to the problem of managing and analyzing the large volumes of data that they generate. Traditional approaches that involve collection of data from IoT devices into one centralized repository for further analysis are not always applicable due to the large amount of collected data, the use of communication channels with limited bandwidth, security and privacy requirements, etc. Federated learning (FL) is an emerging approach that allows one to analyze data directly on data sources and to federate the results of each analysis to yield a result as traditional centralized data processing. FL is being actively developed, and currently, there are several open-source frameworks that implement it. This article presents a comparative review and analysis of the existing open-source FL frameworks, including their applicability in IoT systems. The authors evaluated the following features of the frameworks: ease of use and deployment, development, analysis capabilities, accuracy, and performance. Three different data sets were used in the experiments—two signal data sets of different volumes and one image data set. To model low-power IoT devices, computing nodes with small resources were defined in the testbed. The research results revealed FL frameworks that could be applied in the IoT systems now, but with certain restrictions on their use. View Full-Text
Keywords: federated learning; Internet of Things; smart sensors; distributed learning; machine learning; deep learning; privacy federated learning; Internet of Things; smart sensors; distributed learning; machine learning; deep learning; privacy
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MDPI and ACS Style

Kholod, I.; Yanaki, E.; Fomichev, D.; Shalugin, E.; Novikova, E.; Filippov, E.; Nordlund, M. Open-Source Federated Learning Frameworks for IoT: A Comparative Review and Analysis. Sensors 2021, 21, 167. https://doi.org/10.3390/s21010167

AMA Style

Kholod I, Yanaki E, Fomichev D, Shalugin E, Novikova E, Filippov E, Nordlund M. Open-Source Federated Learning Frameworks for IoT: A Comparative Review and Analysis. Sensors. 2021; 21(1):167. https://doi.org/10.3390/s21010167

Chicago/Turabian Style

Kholod, Ivan, Evgeny Yanaki, Dmitry Fomichev, Evgeniy Shalugin, Evgenia Novikova, Evgeny Filippov, and Mats Nordlund. 2021. "Open-Source Federated Learning Frameworks for IoT: A Comparative Review and Analysis" Sensors 21, no. 1: 167. https://doi.org/10.3390/s21010167

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