ItemConference Paper

A Privacy-Preserving Federated Learning Protocol for Secure Analytics of IoT Sensor Data Using Homomorphic Encryption

Title
A Privacy-Preserving Federated Learning Protocol for Secure Analytics of IoT Sensor Data Using Homomorphic Encryption
Creator
Kavitha, Rose; Gour, Shivashish; Arvind, Tripti; Selvanathan, S.
Description
The proliferation of Internet of Things (IoT) devices has led to massive amounts of sensitive data generation, making data security a paramount concern. Existing methods often struggle with protecting heterogeneous IoT data efficiently, particularly during model training and communication. In this work, we propose a federated learning framework integrated with secure encryption mechanisms to safeguard IoT data during model training and aggregation. Each client device trains a local model using its own sensor data, encrypts the model parameters, and sends them to a server. The server aggregates the encrypted models and sends back the global model for decryption by the clients, ensuring data privacy throughout the process. The proposed framework reduces the unauthorized access risks and also the experimental results demonstrate that the model results in an accuracy of 92% during prediction tasks. The system's encryption overhead was minimal, with only a 7.5% increase in computation time compared to unencrypted federated learning methods. Future work will focus on optimizing the encryption techniques for resource-constrained IoT devices and exploring adaptive security mechanisms powered by machine learning to detect emerging threats dynamically. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
Source
Lecture Notes in Electrical Engineering;Volume;1529 LNEE;pp.169-174
Date
01-01-2026
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Federated Learning; Homomorphic Encryption; Internet of Things (IoT); Secure IoT
Coverage
Kavitha R., Department of Management Studies, New Horizon College of Engineering, Outer Ring Road, Marathahalli, Kadabeesanahalli, Bengaluru, 560103, India; Gour S., Department of Computer Science and Engineering - BCT, Jain (Deemed-to-be) University, 45th km, NH - 209, Jakkasandra Post, Kanakapura Taluk, Ramanagara District, Karnataka, Bengaluru, 562112, India; Arvind T., School of Business and Management, Christ University, Hosur Main Road, Karnataka, Bengaluru, 560029, India, VHNSN College, Tamil Nadu, Virudhunagar, 626001, India; Selvanathan S., VHNSN College, Tamil Nadu, Virudhunagar, 626001, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 18761100; ISBN: 978-981955834-6;
Format
online
Language
English
Type
Conference paper

Citation

Kavitha, Rose; Gour, Shivashish; Arvind, Tripti; Selvanathan, S., “A Privacy-Preserving Federated Learning Protocol for Secure Analytics of IoT Sensor Data Using Homomorphic Encryption,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25449.

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