The rapid expansion of IoT ecosystems introduces severe challenges in scalability, security, and real-time decision-making. Traditional centralized architectures struggle with latency, privacy concerns, and excessive resource consumption, making them unsuitable for modern large-scale IoT deployments. This paper presents a novel Federated Learning-driven Large Language Model (FL-LLM) framework, designed to enhance IoT system intelligence while ensuring data privacy and computational efficiency. The framework integrates Generative IoT (GIoT) models with a Gradient Sensing Federated Strategy (GSFS), dynamically optimizing model updates based on real-time network conditions. By leveraging a hybrid edge-cloud processing architecture, our approach balances intelligence, scalability, and security in distributed IoT environments. Evaluations on the IoT-23 dataset demonstrate that our framework improves model accuracy, reduces response latency, and enhances energy efficiency, outperforming traditional FL techniques (i.e., FedAvg, FedOpt). These findings highlight the potential of integrating LLM-powered federated learning into large-scale IoT ecosystems, paving the way for more secure, scalable, and adaptive IoT management solutions.

本研究针对物联网生态系统扩展带来的可扩展性、安全性和实时决策难题，提出了一种新颖的基于联邦学习的大型语言模型框架（FL-LLM），旨在提升物联网系统智能并确保数据隐私和计算效率。研究表明，该框架通过混合边缘-云处理架构，优化模型更新，提高模型准确性，降低响应延迟和能耗，突显了在大规模物联网中集成LLM驱动的联邦学习的潜力。

大型语言模型与联邦学习结合实现可扩展和安全的物联网管理