Synergizing Intelligence and Privacy: A Review of Integrating Internet of Things, Large Language Models, and Federated Learning in Advanced Networked Systems Article Swipe
Hong Wei Yang
,
Hao Liu
,
Xin Yuan
,
Kai Wu
,
Wei Ni
,
J. Andrew Zhang
,
Ren Ping Liu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app15126587
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/app15126587
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.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.3390/app15126587
- https://www.mdpi.com/2076-3417/15/12/6587/pdf?version=1749730968
- OA Status
- gold
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- 1
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- OpenAlex ID
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All OpenAlex metadata
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https://doi.org/10.3390/app15126587Digital Object Identifier
- Title
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Synergizing Intelligence and Privacy: A Review of Integrating Internet of Things, Large Language Models, and Federated Learning in Advanced Networked SystemsWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-06-11Full publication date if available
- Authors
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Hong Wei Yang, Hao Liu, Xin Yuan, Kai Wu, Wei Ni, J. Andrew Zhang, Ren Ping LiuList of authors in order
- Landing page
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https://doi.org/10.3390/app15126587Publisher landing page
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https://www.mdpi.com/2076-3417/15/12/6587/pdf?version=1749730968Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2076-3417/15/12/6587/pdf?version=1749730968Direct OA link when available
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1Total citation count in OpenAlex
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| referenced_works | https://openalex.org/W4319778953, https://openalex.org/W3159080474, https://openalex.org/W2767079719, https://openalex.org/W4402794779, https://openalex.org/W4386245189, https://openalex.org/W3103802018, https://openalex.org/W4406800520, https://openalex.org/W2624989916, https://openalex.org/W2132172731, https://openalex.org/W4401857637, https://openalex.org/W4390872887, https://openalex.org/W4404721440, https://openalex.org/W4401043467, https://openalex.org/W4385627126, https://openalex.org/W4393307007, https://openalex.org/W4394862623, https://openalex.org/W4385572063, https://openalex.org/W2141420453, https://openalex.org/W1873763122, https://openalex.org/W4313017734, https://openalex.org/W3109340983, https://openalex.org/W4293812121, https://openalex.org/W3111919937, https://openalex.org/W3177095755, https://openalex.org/W4385626884, https://openalex.org/W4394627330, https://openalex.org/W4396736351, https://openalex.org/W3003484616, https://openalex.org/W4389520700, https://openalex.org/W3046449784, https://openalex.org/W4392903998, https://openalex.org/W3141585064, https://openalex.org/W4407230907, https://openalex.org/W4402350363, https://openalex.org/W4401545746, https://openalex.org/W4392904059, https://openalex.org/W4404782226, https://openalex.org/W4388209069, https://openalex.org/W4214607237, https://openalex.org/W4407319925, https://openalex.org/W4376149513, https://openalex.org/W4327643804, https://openalex.org/W2900067643, https://openalex.org/W3135725526, https://openalex.org/W4390828922, https://openalex.org/W4386071707, https://openalex.org/W4378509449, https://openalex.org/W4285601829, https://openalex.org/W4396629473, https://openalex.org/W2473418344, https://openalex.org/W3102031770, https://openalex.org/W2792152774, https://openalex.org/W4384918640, https://openalex.org/W3202088367, https://openalex.org/W4291653324, https://openalex.org/W4313325044, https://openalex.org/W2963456518, https://openalex.org/W3177173743, https://openalex.org/W4385325027, https://openalex.org/W4385764383, https://openalex.org/W3047608784, https://openalex.org/W3155963862, https://openalex.org/W4405429486, https://openalex.org/W4404608635, https://openalex.org/W4409129037, https://openalex.org/W4372260089, https://openalex.org/W4390874616, https://openalex.org/W4386766438, https://openalex.org/W4316661314, https://openalex.org/W4406042156, https://openalex.org/W2805611890, https://openalex.org/W4401863415, https://openalex.org/W4390871644, https://openalex.org/W4402671706, https://openalex.org/W4319663761, https://openalex.org/W4392173735, https://openalex.org/W4385805259, https://openalex.org/W4406163678, https://openalex.org/W4290948380, https://openalex.org/W4393252606, https://openalex.org/W4396705938, https://openalex.org/W4390872376, https://openalex.org/W4383337653, https://openalex.org/W2896235627, https://openalex.org/W4404688019 |
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