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

Ivan Kholod¹, Evgeny Yanaki¹, Dmitry Fomichev¹

¹Faculty of Computer Science and Technology, Saint Petersburg Electrotechnical University "LETI", Saint Petersburg 197376, Russia.

Sensors (Basel, Switzerland)

|January 1, 2021

Summary

This summary is machine-generated.

Federated learning (FL) offers a solution for analyzing Internet of Things (IoT) data without centralizing it. This study reviews open-source FL frameworks, finding some applicable to IoT with limitations.

Keywords:

Internet of Things deep learning distributed learning federated learning machine learning privacy smart sensors

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Area of Science:

Computer Science
Data Science
Machine Learning

Background:

Internet of Things (IoT) systems generate vast data volumes, posing challenges for traditional centralized analysis.
Limitations of centralized data analysis include data volume, bandwidth constraints, and security/privacy concerns.
Federated learning (FL) enables decentralized data analysis directly on edge devices.

Purpose of the Study:

To conduct a comparative review and analysis of open-source federated learning (FL) frameworks.
To evaluate the applicability of these FL frameworks in Internet of Things (IoT) systems.
To assess FL frameworks based on ease of use, deployment, development, accuracy, and performance.

Main Methods:

Comparative analysis of existing open-source federated learning (FL) frameworks.
Experimental evaluation using three datasets: two signal datasets (varying volumes) and one image dataset.
Simulation of low-power IoT devices using computing nodes with limited resources.

Main Results:

Identified specific open-source FL frameworks suitable for current IoT applications.
Performance and accuracy varied across FL frameworks and dataset types.
Resource constraints of simulated IoT devices impacted framework performance.

Conclusions:

Certain open-source FL frameworks can be deployed in IoT systems presently.
The application of these FL frameworks in IoT is subject to specific usage restrictions.
Further development is needed to optimize FL frameworks for resource-constrained IoT environments.