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

Abstract

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.

New Directions in Sensor Research: A Bibliometric Analysis for Detecting Emerging Research Fields and New Technological Trajectories

“…In particular, sensor system can be considered a parasite technology of other technological systems (Coccia, 2019;2019a;Coccia and Watts, 2020). The parasitic technologies of sensors are systems that interact with the ecological system of the host (or master) technology (Elsisi et al, 2021;Kholod et al, 2021;Pereira et al, 2017). For instance, the sensors of inertial measuring unit and global positioning system are parasite technologies when installed in wearable (host) technology of consumer sports (Aroganam et al, 2019).…”

Scientific Developments and New Technological Trajectories in Sensor Research

“…This study reveals that technological development of sensors is due to evolutionary pathways based on interactions of sensors with other technological systems, such as information and communication technologies, artificial intelligence, Internet of Things, etc. [ 33 , 34 , 36 , 43 , 45 , 46 , 49 , 50 ] (cf., also [ 28 , 47 , 48 , 54 , 116 , 117 , 118 , 119 , 120 , 121 , 122 ]). Results suggest that sensors have, as parasite technologies (i.e., depending on other technologies; [ 17 ]), a wide spectrum of applications in medicine, environmental pollution, aircraft and automotive industries [ 123 , 124 , 125 , 126 , 127 ].…”

“…because they are embodied in other technological systems [ 19 , 20 ]. In general, sensor technologies have multi-mode interactions with other technologies that support a co-evolution of inter-related technological systems and new evolutionary pathways of technological trajectories [ 17 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ]. One main example is smart sensors, which co-evolve through complex interaction with artificial intelligence technologies, Bluetooth technology, medical technologies, cloud computing, etc.…”

Analyzing Patient Trajectories With Artificial Intelligence

“…Although these provide valuable tools for developers, we foresee more research that tailors them to the context of patient trajectories (eg, by offering sequential models for longitudinal data). More importantly, the availability of software packages [ 60 , 61 ] that allow both simulation of federated learning scenarios and their deployment in real clinical settings will accelerate the adoption of federated or distributed learning approaches and open a wide array of research exploration and experimentation possibilities. This will eventually yield longitudinal trajectory analyses that span patient journeys across multiple hospitals or health care institutions.…”