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.
Highlights
The Internet of Things (IoT) [1] combines a large number of smart devices that produce large volumes of data
In contrast to the ABY3 and SecureML protocols, the data owners participate in the training process by performing the training process locally and sending encrypted model weights to the aggregator, which implements the aggregation of the network gradients [28]
For the image data set, Paddle Federated Learning (PFL) showed the best accuracy of all Federated learning (FL) frameworks; it is 10% lower than TF’s accuracy and 10% higher than PyTorch’s accuracy
Summary
The Internet of Things (IoT) [1] combines a large number of smart devices (sensors, controllers, smartphones, etc.) that produce large volumes of data. The important drawback of collecting data in the centralized data warehouses is that this leads to an increase in total processing time, network traffic, and risk of unauthorized access to the data Another aspect of application of AI-based technologies relates to the security and privacy of the personal data being collected. In [12], the authors focused on poisoning attacks on federated multi-task learning and evaluated different attack scenarios, including alteration of raw data on target devices (direct attacks) and indirect modification of data on target devices using communication protocols. In this survey, the authors investigated existing open-source FL frameworks:.
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