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All Journal Jurnal Info Sains : Informatika dan Sains
Richand Pamilano
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Computer Science & IT

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Leveraging Federated Learning and Edge Computing for Privacy-Preserving Real-Time Anomaly Detection in IoT Networks Arpan Hendri Batuara; Sri Jenni Rejeki Situringkir; Rangga Ramadia; Richand Pamilano; Sinek Mehuli BR Perangin Angin; Devita Permata Sari BR Ginting
Jurnal Info Sains : Informatika dan Sains Vol. 15 No. 01 (2025): Informatika dan Sains , 2025
Publisher : SEAN Institute

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Abstract

The rapid proliferation of Internet of Things (IoT) networks has heightened the need for robust, privacy-preserving security mechanisms that ensure real-time anomaly detection. This article explores the integration of federated learning (FL) and edge computing as a promising approach to address challenges related to privacy, latency, and resource constraints in IoT environments. Employing a qualitative research methodology, this study analyzes existing literature and emerging frameworks to comprehensively assess the advantages, challenges, and future research directions of applying FL and edge computing for anomaly detection in IoT. Findings highlight that FL combined with lightweight anomaly detection algorithms deployed at the edge can significantly enhance privacy while ensuring timely intrusion detection, despite heterogeneity and limited device resources. The study suggests pathways for developing adaptive, scalable, and secure IoT networks leveraging these paradigms.
Co-Authors Arpan Hendri Batuara Devita Permata Sari BR Ginting Rangga Ramadia Sinek Mehuli BR Perangin Angin Sri Jenni Rejeki Situringkir