# LLMs meet Federated Learning for Scalable and Secure IoT Management

arXiv: https://arxiv.org/abs/2504.16032 · HTML: https://mlsanity.com/paper/2504.16032/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 16 references that are in the map.
- First posted on arXiv 22 Apr 2025.

## Builds on (most cited first, 6 of 6)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations](https://mlsanity.com/paper/2409.05976/) (2024)
- [OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning](https://mlsanity.com/paper/2402.06954/) (2024)
- [Integrating Large Language Models with Internet of Things Applications](https://mlsanity.com/paper/2410.19223/) (2024)
- [Edge, Fog, and Cloud Computing : An Overview on Challenges and Applications](https://mlsanity.com/paper/2211.01863/) (2022)
- [Energy-Aware LLMs: A step towards sustainable AI for downstream applications](https://mlsanity.com/paper/2503.17783/) (2025)

## Built on by (most cited first, 6 of 6)

- [LLM-Based Threat Detection and Prevention Framework for IoT Ecosystems](https://mlsanity.com/paper/2505.00240/) (2025)
- [Hybrid LLM-Enhanced Intrusion Detection for Zero-Day Threats in IoT Networks](https://mlsanity.com/paper/2507.07413/) (2025)
- [Edge-FIT: Federated Instruction Tuning of Quantized LLMs for Privacy-Preserving Smart Home Environments](https://mlsanity.com/paper/2510.03284/) (2025)
- [Open-Source LLM-Driven Federated Transformer for Predictive IoV Management](https://mlsanity.com/paper/2505.00651/) (2025)
- [Phishing Detection in the Gen-AI Era: Quantized LLMs vs Classical Models](https://mlsanity.com/paper/2507.07406/) (2025)
- [Federated Prompt Learning: A Unified Framework, Empirical Analysis, and Future Directions](https://mlsanity.com/paper/2608.13844/) (2026)

## Abstract (arXiv)

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.