mlsanityData to 14 Oct 2026

LLMs meet Federated Learning for Scalable and Secure IoT Management

arXiv 2504.16032 ↗First posted 22 Apr 2025cs.LG cs.AI cs.ETcited by 6

Not on the map. Why?

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.

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

6 of 16 references.

References · 6

  1. 2019Advances and Open Problems in Federated Learningcited by 3,046
  2. 2024FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptationscited by 146
  3. 2024OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learningcited by 128
  4. 2024Integrating Large Language Models with Internet of Things Applicationscited by 7
  5. 2022Edge, Fog, and Cloud Computing : An Overview on Challenges and Applicationscited by 3
  6. 2025Energy-Aware LLMs: A step towards sustainable AI for downstream applicationscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 1.

6 papers.

  1. 2025LLM-Based Threat Detection and Prevention Framework for IoT Ecosystemscited by 6
  2. 2025Hybrid LLM-Enhanced Intrusion Detection for Zero-Day Threats in IoT Networkscited by 2
  3. 2025Edge-FIT: Federated Instruction Tuning of Quantized LLMs for Privacy-Preserving Smart Home Environmentscited by 2
  4. 2025Open-Source LLM-Driven Federated Transformer for Predictive IoV Managementcited by 1
  5. 2025Phishing Detection in the Gen-AI Era: Quantized LLMs vs Classical Modelscited by 1
  6. 2026Federated Prompt Learning: A Unified Framework, Empirical Analysis, and Future Directionscited by 0