LLMs meet Federated Learning for Scalable and Secure IoT Management
This work addresses scalability and security problems for large-scale IoT deployments, offering an incremental hybrid method.
This paper tackles the challenges of scalability, security, and real-time decision-making in IoT ecosystems by proposing a Federated Learning-driven Large Language Model (FL-LLM) framework. The result shows improved model accuracy, reduced response latency, and enhanced energy efficiency on the IoT-23 dataset, outperforming traditional FL techniques like FedAvg and FedOpt.
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.