用联邦学习让大模型更安全高效地管理物联网。
LLMs meet Federated Learning for Scalable and Secure IoT Management
- 将大模型与联邦学习结合，动态调整更新策略。
- 在IoT-23数据集上准确率更高，延迟更低，更省电。
- 适合需要隐私保护的大规模物联网部署场景。
物联网生态的快速扩展带来了可扩展性、安全性和实时决策的严峻挑战。传统集中式架构在延迟、隐私和资源消耗方面表现不佳，难以适应现代大规模物联网部署。本文提出一种新型联邦学习驱动的大语言模型框架（FL-LLM），旨在提升物联网系统智能水平的同时保障数据隐私和计算效率。该框架融合生成式物联网（GIoT）模型与梯度感知联邦策略（GSFS），根据实时网络状况动态优化模型更新。通过混合边缘-云处理架构，本方法在分布式物联网环境中实现了智能、可扩展性与安全性的平衡。在IoT-23数据集上的评估表明，该框架在模型准确率、响应延迟和能效方面均优于传统联邦学习方法（如FedAvg、FedOpt），凸显了将大模型赋能的联邦学习应用于大规模物联网生态系统的潜力，为更安全、可扩展、自适应的物联网管理提供了新路径。
原文摘要 · Abstract (English)
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.
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