Open Access

ARTICLE

A Novel Clustered Distributed Federated Learning Architecture for Tactile Internet of Things Applications in 6G Environment

Omar Alnajar^*, Ahmed Barnawi

Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 80221, Saudi Arabia

* Corresponding Author: Omar Alnajar. Email:

(This article belongs to the Special Issue: Next-Generation Intelligent Networks and Systems: Advances in IoT, Edge Computing, and Secure Cyber-Physical Applications)

Computer Modeling in Engineering & Sciences 2025, 143(3), 3861-3897. https://doi.org/10.32604/cmes.2025.065833

Received 22 March 2025; Accepted 09 June 2025; Issue published 30 June 2025

Abstract

The Tactile Internet of Things (TIoT) promises transformative applications—ranging from remote surgery to industrial robotics—by incorporating haptic feedback into traditional IoT systems. Yet TIoT’s stringent requirements for ultra-low latency, high reliability, and robust privacy present significant challenges. Conventional centralized Federated Learning (FL) architectures struggle with latency and privacy constraints, while fully distributed FL (DFL) faces scalability and non-IID data issues as client populations expand and datasets become increasingly heterogeneous. To address these limitations, we propose a Clustered Distributed Federated Learning (CDFL) architecture tailored for a 6G-enabled TIoT environment. Clients are grouped into clusters based on data similarity and/or geographical proximity, enabling local intra-cluster aggregation before inter-cluster model sharing. This hierarchical, peer-to-peer approach reduces communication overhead, mitigates non-IID effects, and eliminates single points of failure. By offloading aggregation to the network edge and leveraging dynamic clustering, CDFL enhances both computational and communication efficiency. Extensive analysis and simulation demonstrate that CDFL outperforms both centralized FL and DFL as the number of clients grows. Specifically, CDFL demonstrates up to a 30% reduction in training time under highly heterogeneous data distributions, indicating faster convergence. It also reduces communication overhead by approximately 40% compared to DFL. These improvements and enhanced network performance metrics highlight CDFL’s effectiveness for practical TIoT deployments. These results validate CDFL as a scalable, privacy-preserving solution for next-generation TIoT applications.

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Keywords

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