LLM 携手联邦学习，实现可扩展且安全的 IoT 管理
LLMs meet Federated Learning for Scalable and Secure IoT Management
AI总结：
针对大规模 IoT 的延迟、隐私与资源挑战，本文提出融合 GIoT、GSFS 和边云架构的 FL-LLM 框架，在 IoT-23 上优于 FedAvg 与 FedOpt。
摘要：
IoT 生态系统的快速扩张在可扩展性、安全性和实时决策方面带来了严峻挑战。传统集中式架构受困于延迟、隐私问题和过度的资源消耗，不适合现代大规模 IoT 部署。本文提出一种新颖的联邦学习驱动的大语言模型（FL-LLM）框架，旨在增强 IoT 系统智能，同时确保数据隐私和计算效率。该框架将生成式 IoT（GIoT）模型与梯度感知联邦策略（GSFS）相结合，根据实时网络状况动态优化模型更新。通过利用边云混合处理架构，本方法在分布式 IoT 环境中平衡智能、可扩展性和安全性。在 IoT-23 数据集上的评估表明，该框架提高了模型准确率，降低了响应延迟，并提升了能源效率，优于传统 FL 技术（即 FedAvg、FedOpt）。这些发现凸显了将基于 LLM 的联邦学习集成到大规模 IoT 生态系统中的潜力，为更安全、可扩展和自适应的 IoT 管理解决方案铺平了道路。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
