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

Computer Science > Artificial Intelligence [Submitted on 21 Apr 2026] Towards Energy Impact on AI-Powered 6G IoT Networks: Centralized vs. Decentralized Anjie Qiu, Donglin Wang, Sanket Partani, Andreas Weinand, Hans D. Schotten 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. Comments: 6 pages, 4 figures. Accepted for presentation at the IEEE GLOBECOM 2025 Workshop on Workshop on Green Learning for Wireless Communications Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.19377 [cs.AI]   (or arXiv:2604.19377v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.19377 Focus to learn more Submission history From: Anjie Qiu [view email] [v1] Tue, 21 Apr 2026 11:59:08 UTC (2,544 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)