## 1 Introduction
* Search Keywords: A combination of keywords was used, including, but not limited to \"Internet of Things\" OR \"IoT\" AND \"Large Language Models\" OR \"LLMs\" AND \"Federated Learning\" OR \"FL\"; \"LLMs on edge devices\"; \"Federated LLMs for IoT\"; \"privacy-preserving LLMs in IoT\"; \"LoRA for Federated Learning\"; \"Split Federated Learning for LLMs\"; \"efficient LLM deployment on IoT\"; \"AIoT AND LLMs\"; \"Industrial IoT AND Federated Learning\". * Inclusion Criteria: Papers were included if they were peer-reviewed journal articles, conference proceedings, or highly cited pre-prints directly relevant to the integration of IoT, LLMs, and FL. We prioritized studies that discussed system architectures, method  
Figure 2: Overview of the review’s organizational structure. The diagram outlines the paper’s progression through its main sections: Foundational Concepts (Section 2), Architectures (Section 3), FL Methodologies (Section 4), Synergistic Effects (Section 5), Challenges and Mitigation (Section 6), Research Gaps and Future Directions (Section 7), and Conclusion (Section 8). Key topics within each section are indicated, offering a reader roadmap. ologies, applications, challenges, or future directions related to this tripartite synergy, particularly those addressing resource constraints and privacy in IoT environments. * Exclusion Criteria: Papers were excluded if they focused solely on one technology without significant discussion of its integration with the other two, were not written in English, or were not accessible in full text. Short abstracts, posters, and non-academic articles were also excluded. * Literature Screening and Selection Statistics: Our initial search across the specified databases (Google Scholar, IEEE Xplore, ACM Digital Library, Scopus, and ArXiv using keywords such as ((\"Internet of Things\" OR \"IoT\") AND (\"Large Language Models\" OR \"LLMs\") AND (\"Federated Learning\" OR \"FL\")) AND (\"Edge Computing\" OR \"Privacy\")) yielded 223 unique articles. After screening titles and abstracts for relevance to the tripartite synergy of IoT, LLMs, and FL, particularly in edge environments, 160 articles were retained. These 160 articles underwent a full-text review against our predefined inclusion and exclusion criteria. From this detailed assessment, 78 articles were identified as directly pertinent to the core research questions of this review and were selected for in-depth data extraction and synthesis. The final manuscript cites a total of 135 references, which encompass these 78 core articles along with foundational papers and other supporting literature. * Bias Assessment and Mitigation: To ensure a balanced review, potential sources of bias were considered. Publication bias, the tendency to publish positive or significant results, was mitigated by including pre-prints from ArXiv, allowing for the inclusion of recent and ongoing research that may not yet have undergone peer review. To counteract database bias, we utilized multiple prominent and diverse academic databases. Furthermore, keyword bias was addressed by developing a comprehensive list of search terms, including synonyms and variations, related to IoT, LLMs, FL, and their intersection with edge computing and privacy. The selection and data extraction were primarily conducted by two authors, with discrepancies resolved through discussion to minimize individual researcher bias. * Data Extraction and Synthesis: Relevant information regarding methodologies, challenges, proposed solutions, applications, and future trends was extracted from the selected papers. This information was then synthesized to identify common themes, research gaps, and the overall state of the art, forming the basis of this review.