FedMicro-IDA: A Federated Learning and Microservices-based Framework for IoT Data Analytics
Safa Ben Atitallah, Maha Driss, and Henda Ben Ghezela

TL;DR
This paper introduces FedMicro-IDA, a federated learning framework built on microservices architecture for IoT data analytics, enhancing privacy, reducing latency, and improving malware detection accuracy.
Contribution
It proposes a novel microservices-based federated learning architecture tailored for IoT, validated through malware detection experiments with superior performance.
Findings
Achieved 99.24% detection accuracy.
Outperformed existing methods in malware classification.
Reduced latency and bandwidth usage in IoT analytics.
Abstract
The Internet of Things (IoT) has recently proliferated in both size and complexity. Using multi-source and heterogeneous IoT data aids in providing efficient data analytics for a variety of prevalent and crucial applications. To address the privacy and security concerns raised by analyzing IoT data locally or in the cloud, distributed data analytics techniques were proposed to collect and analyze data in edge or fog devices. In this context, federated learning has been recommended as an ideal distributed machine/deep learning-based technique for edge/fog computing environments. Additionally, the data analytics results are time-sensitive; they should be generated with minimal latency and high reliability. As a result, reusing efficient architectures validated through a high number of challenging test cases would be advantageous. The work proposed here presents a solution using a…
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