分布式机器学习可降低6G物联网能耗70%，提升能效。
Towards Energy Impact on AI-Powered 6G IoT Networks: Centralized vs. Decentralized
- 对比集中式与分布式学习的能耗模型，优化训练与传输
- 分布式模型达90%预测准确率，能耗降低70%
- 适合关注绿色AI与铁路智能运维的研究者
第六代移动通信（6G）技术为物联网（IoT）网络中的机器学习（ML）应用带来了新的挑战与机遇，尤其在能源效率方面。模型训练与数据传输显著影响能耗，因此优化这些过程对可持续系统设计至关重要。本研究首先分析了集中式与去中心化架构的能耗模型，并在德国铁路基础设施中部署测试平台，利用传感器数据进行基于ML的预测性维护。对分布式学习与集中式学习（CL）架构的对比分析表明，分布式模型在保持约90%预测准确率的同时，整体电力消耗最多降低70%。这些发现凸显了分布式机器学习在实际物联网部署中提升能效的潜力，特别是通过降低传输相关的能耗成本。
原文摘要 · Abstract (English)
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.
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