An Optimized Federated Learning Algorithm for Decentralized Intrusion Detection Systems Using Artificial Bee Colony Optimization

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 10

Year of Publication : 2025

Authors : Shourya Shukla, Ajay Singh Raghuvanshi, Saikat Majumder

10.14445/23488549/IJECE-V12I10P118

How to Cite?

Shourya Shukla, Ajay Singh Raghuvanshi, Saikat Majumder, "An Optimized Federated Learning Algorithm for Decentralized Intrusion Detection Systems Using Artificial Bee Colony Optimization," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 10, pp. 231-251, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I10P118

Abstract:

With the advancement in IoT technologies, Wireless Sensor Networks have found many applications in the modern era. Due to this, the malicious activities in the networks have seen a major surge. Data theft and manipulation have been a serious concern among researchers. In real-time scenarios, the data communication from nodes to the servers increases the communication overhead and makes the network vulnerable to attacks. A decentralized detection strategy has become a necessity to detect these intrusions efficiently. Federated Learning algorithms have been a major choice for decentralized learning frameworks. The federated models learn the data patterns based on the trained local models. In this paper, a novel model aggregation strategy has been proposed. The weightage or local share of each client is optimized using the Artificial Bee Colony Optimization algorithm, called the optimized local share. The optimized local share has been utilized for three neural network architectures with five-layer deep structures. A Fully Connected Network, a Long Short-Term Memory network, and a hybrid network were employed to detect intrusions in the network. The NSL-KDD and UNSW-NB15 datasets have been distributed into 5, 10, and 20 clients for local training and aggregated using optimized local shares. Binary and multi-class classification achieved high accuracies, comparable to State-of-the-Art frameworks and centralized learning models, while ensuring data privacy and integrity of each client.

Keywords:

Federated Learning, Intrusion Detection Systems, Artificial Bee Colony Optimization, Deep learning, Model Aggregation.

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