International Journal on Science and Technology

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A Hierarchical Federated Learning Framework for Secure and Scalable IoT Ecosystems: System Design, Implementation, and Performance Analysis

Author(s) Dr. Ashish Rai, Ms. Ruchita Mathur
Country India
Abstract The increasing use of Internet of Things (IoT) devices in various application areas has led to the creation of unprecedented capabilities in data generation, while also giving rise to fundamental challenges in terms of scalability, security, and intelligent processing. The traditional centralized cloud model faces latency constraints and privacy risks associated with the large-scale deployment of IoT devices [1], [2]. This paper proposes a new hierarchical federated learning (HFL) system specifically tailored for use in heterogeneous IoT settings, which combines edge intelligence with blockchain-secured communication channels to overcome the above-stated fundamental limitations [6]. Our system implementation includes adaptive model aggregation techniques and differential privacy mechanisms to achieve a balance between learning accuracy and privacy preservation [10].
By conducting extensive experimentation across three diverse IoT application domains, namely smart healthcare, industrial automation, and city infrastructure, we show the efficacy of our framework in achieving an average latency reduction of 47% over traditional cloud-centric designs while preserving model accuracy within 3.2% of the centralized baselines [3], [4]. Our proposed design framework proves to be especially useful in resource-scarce settings, where it achieves a 34% decrease in energy expenditure during training phases while also being able to enforce effective security measures against potential threats.
Keywords Internet of Things 1, Federated Learning 2, Edge Computing 3, IoT Security 4, Artificial Intelligence of Things (AIoT) 5, Privacy-Preserving Analytics 6.
Field Computer
Published In Volume 17, Issue 1, January-March 2026
Published On 2026-02-20
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