FedMicro-IDA: A Federated Learning and Microservices-based Framework for IoT Data Analytics

2025-10-22

AI Summary

To address the challenge of efficiently analyzing multi-source heterogeneous IoT data under stringent constraints of privacy preservation, low latency, and high reliability, this paper proposes the first edge intelligence analytics framework integrating federated learning with a microservice architecture. The framework enables dynamic orchestration and reuse of intelligent microservices, unifying collaborative model training and elastic system scalability. It employs a Docker/Kubernetes-based microservice deployment infrastructure augmented by edge computing to minimize communication latency and bandwidth consumption. Experimental evaluation on the MaleVis dataset demonstrates that the proposed method achieves a malware detection accuracy of 99.24%, significantly outperforming existing approaches. Crucially, it ensures data locality and model privacy security throughout the learning process. The results validate the framework’s comprehensive advantages in analytical performance, scalability, and privacy protection.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Application Domains: Internet of Things, Sensor Networks & Smart Cities
• Intelligent Robots: Multi-Robot Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Data transparency and provenance

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 microservices-based architecture that allows an IoT application to be structured as a collection of fine-grained, loosely coupled, and reusable entities. The proposed solution uses the promising capabilities of federated learning to provide intelligent microservices that ensure efficient, flexible, and extensible data analytics. This solution aims to deliver cloud calculations to the edge to reduce latency and bandwidth congestion while protecting the privacy of exchanged data. The proposed approach was validated through an IoT-malware detection and classification use case. MaleVis, a publicly available dataset, was used in the experiments to analyze and validate the proposed approach. This dataset included more than 14,000 RGB-converted images, comprising 25 malware classes and one benign class. The results showed that our proposed approach outperformed existing state-of-the-art methods in terms of detection and classification performance, with a 99.24%.

Problem

• Develops federated learning framework for IoT data privacy
• Uses microservices architecture to reduce analytics latency
• Validates malware detection via distributed image classification

Innovation

• Federated learning for privacy-preserving distributed analytics
• Microservices architecture enabling modular reusable components
• Edge computing deployment reducing latency and bandwidth usage