Title: Energy-Efficient Quantized Federated Learning for Resource-constrained IoT devices Show Chinese title

Title: 面向资源受限物联网设备的节能量化联邦学习

Abstract: 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. Show Chinese abstract

Abstract: 联邦学习（FL）已成为一种有前景的范式，可以在保护数据隐私的同时实现协作机器学习，使其特别适用于物联网（IoT）环境。 然而，资源受限的物联网设备由于能量有限、通信信道不可靠以及假设无限块长度传输不切实际，面临重大挑战。 本文提出了一种用于物联网网络的联邦学习框架，该框架结合了有限块长度传输、模型量化和误差感知聚合机制，以提高能效和通信可靠性。 该框架还优化了上行链路传输功率，以平衡节能和模型性能。 仿真结果表明，与标准FL模型相比，所提出的方法可将能耗降低多达75%，同时保持强大的模型准确性，使其成为在资源受限的实际物联网场景中进行FL的可行解决方案。 这项工作为在实际物联网部署中实现高效可靠的FL铺平了道路。 索引术语： 联邦学习，物联网，有限块长度，量化，能效。