LLMs meet Federated Learning for Scalable and Secure IoT Management

2025-04-22

AI Summary

To address scalability, security, and real-time decision-making challenges in large-scale IoT systems, this paper proposes a Federated Learning-driven Large Language Model (FL-LLM) framework that establishes an edge–cloud collaborative, privacy-preserving distributed intelligence paradigm. Our key contributions are: (1) the first integrated FL-LLM architecture unifying federated learning with generative LLMs for IoT; (2) Gradient-Aware Federated Strategy (GSFS), a dynamic model update mechanism that adapts to network conditions via gradient sensitivity analysis; and (3) the Generative IoT (GIoT) model, enabling semantic-level device understanding and cross-device collaborative reasoning. Evaluated on the IoT-23 dataset, FL-LLM achieves 12.7% higher accuracy, 41.3% lower end-to-end latency, and 36.5% reduced node energy consumption compared to FedAvg—outperforming state-of-the-art federated learning baselines across all metrics.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Natural Language Processing: (Large) Language Models
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Enhancing IoT system intelligence with privacy and efficiency
• Optimizing model updates for real-time network conditions
• Balancing intelligence, scalability, and security in IoT

Innovation

• FL-LLM framework for IoT privacy and efficiency
• Gradient Sensing Federated Strategy optimizes updates
• Hybrid edge-cloud balances intelligence and security