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| Title | Synergizing Intelligence And Privacy: A Review Of Integrating Internet Of Things, Large Language Models, And Federated Learning In Advanced Networked Systems |
|---|---|
| ID_Doc | 54221 |
| Authors | Yang H.; Liu H.; Yuan X.; Wu K.; Ni W.; Zhang J.A.; Liu R.P. |
| Year | 2025 |
| Published | Applied Sciences (Switzerland), 15, 12 |
| DOI | http://dx.doi.org/10.3390/app15126587 |
| Abstract | Bringing together the Internet of Things (IoT), LLMs, and Federated Learning (FL) offers exciting possibilities, creating a synergy to build smarter, privacy-preserving distributed systems. This review explores the merging of these technologies, particularly within edge computing environments. We examine current architectures and practical methods enabling this fusion, such as efficient low-rank adaptation (LoRA) for fine-tuning large models and memory-efficient Split Federated Learning (SFL) for collaborative edge training. However, this integration faces significant hurdles: the resource limitations of IoT devices, unreliable network communication, data heterogeneity, diverse security threats, fairness considerations, and regulatory demands. While other surveys cover pairwise combinations, this review distinctively analyzes the three-way synergy, highlighting how IoT, LLMs, and FL working in concert unlock capabilities unattainable otherwise. Our analysis compares various strategies proposed to tackle these issues (e.g., federated vs. centralized, SFL vs. standard FL, DP vs. cryptographic privacy), outlining their practical trade-offs. We showcase real-world progress and potential applications in domains like Industrial IoT and smart cities, considering both opportunities and limitations. Finally, this review identifies critical open questions and promising future research paths, including ultra-lightweight models, robust algorithms for heterogeneity, machine unlearning, standardized benchmarks, novel FL paradigms, and next-generation security. Addressing these areas is essential for responsibly harnessing this powerful technological blend. © 2025 by the authors. |
| Author Keywords | data heterogeneity; distributed systems; edge computing; Federated Learning (FL); Internet of Things (IoT); Large Language Model (LLM); network security; Parameter-Efficient Fine-Tuning (PEFT); privacy-preserving techniques (PETs); Split Federated Learning (SFL) |
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