Towards Energy Impact on AI-Powered 6G IoT Networks: Centralized vs. Decentralized

TL;DR

This paper analyzes energy consumption in 6G IoT networks, comparing centralized and decentralized ML architectures, and demonstrates that distributed models can significantly reduce energy use while maintaining high accuracy.

Contribution

It provides a detailed energy consumption model and empirical comparison between centralized and decentralized ML architectures in 6G IoT networks, with real-world validation.

Findings

Distributed ML maintains ~90% predictive accuracy.

Distributed models reduce electricity consumption by up to 70%.

Energy efficiency benefits are demonstrated in a German railway testbed.

Abstract

The emergence of sixth-generation (6G) technologies has introduced new challenges and opportunities for machine learning (ML) applications in Internet of Things (IoT) networks, particularly concerning energy efficiency. As model training and data transmission contribute significantly to energy consumption, optimizing these processes has become critical for sustainable system design. This study first conduct analysis on the energy consumption model for both centralized and decentralized architecture and then presents a testbed deployed within the German railway infrastructure, leveraging sensor data for ML-based predictive maintenance. A comparative analysis of distributed versus Centralized Learning (CL) architectures reveals that distributed models maintain competitive predictive accuracy (~90%) while reducing overall electricity consumption by up to 70%. These findings underscore the potential of distributed ML to improve energy efficiency in real-world IoT deployments, particularly by mitigating transmission-related energy costs.