针对物联网设备设计节能量化联邦学习框架，提升能效与通信可靠性。
Energy-Efficient Quantized Federated Learning for Resource-constrained IoT devices
- 结合有限块长传输与模型量化，降低通信能耗。
- 相比标准联邦学习，能耗最高降低75%，精度仍保持稳定。
- 适合资源受限的物联网场景，尤其关注能效与链路不稳问题。
联邦学习（FL）作为一种协同机器学习范式，能在保护数据隐私的同时实现协作训练，特别适用于物联网（IoT）环境。然而，资源受限的物联网设备面临能量有限、通信信道不可靠以及无法假设无限块长传输等挑战。本文提出一种面向物联网网络的联邦学习框架，集成有限块长传输、模型量化和误差感知聚合机制，以提升能效与通信可靠性。该框架还优化上行链路传输功率，在节能与模型性能间取得平衡。仿真结果表明，所提方法相比标准联邦学习模型可将能耗最多降低75%，同时维持稳定的模型精度，为实际物联网场景中的联邦学习部署提供了可行方案。本工作为高效可靠的物联网联邦学习实现铺平了道路。
原文摘要 · Abstract (English)
Federated Learning (FL) has emerged as a promising paradigm for enabling collaborative machine learning while preserving data privacy, making it particularly suitable for Internet of Things (IoT) environments. However, resource-constrained IoT devices face significant challenges due to limited energy,unreliable communication channels, and the impracticality of assuming infinite blocklength transmission. This paper proposes a federated learning framework for IoT networks that integrates finite blocklength transmission, model quantization, and an error-aware aggregation mechanism to enhance energy efficiency and communication reliability. The framework also optimizes uplink transmission power to balance energy savings and model performance. Simulation results demonstrate that the proposed approach significantly reduces energy consumption by up to 75\% compared to a standard FL model, while maintaining robust model accuracy, making it a viable solution for FL in real-world IoT scenarios with constrained resources. This work paves the way for efficient and reliable FL implementations in practical IoT deployments. Index Terms: Federated learning, IoT, finite blocklength, quantization, energy efficiency.
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