## 8 Conclusions  
Before summarizing our findings, it is important to acknowledge certain limitations of this review. While we endeavored to conduct a comprehensive search across multiple prominent databases and included pre-prints to capture the latest advancements, the selection process may be subject to inherent biases. Our exclusion of non-English language articles and the specific keywords chosen might have inadvertently omitted some relevant studies. Furthermore, the field of integrating IoT, LLMs, and FL is exceptionally dynamic; consequently, new developments may have emerged subsequent to our literature search cutoff in May 2025 that are not encompassed in this work. The review's primary focus on the tripartite synergy also means that related pairwise integrations or broader technological aspects might have received less exhaustive coverage than in specialized surveys. These factors should be considered when interpreting the scope and conclusions of this review. Bringing together the IoT, LLMs, and FL creates a powerful combination. This review has explored how this three-way synergy, backed by strong privacy techniques, paves the way for smarter, more responsive, and trustworthy distributed systems, achieving results that are not available when these technologies are used in pairs. We have mapped out the motivations, the edge-focused architectures, the key methods like PEFT and SFL that make it work, and importantly, the significant challenges involved. Making this powerful integration a reality means tackling some tough hurdles head-on. We need to find ways to run demanding LLMs on resource-limited IoT devices using FL, manage data sharing across networks without overwhelming them, handle the inherent diversity in IoT data and systems, and ensure fairness for everyone involved. Above all, protecting user privacy and securing the entire system against attack, all while meeting legal requirements, is absolutely critical. Despite these difficulties, researchers are actively finding solutions. We are seeing progress with techniques like model compression, smarter communication strategies, personalized learning, advanced privacy methods, and robust ways to combine model updates, though finding the right balance is always key. Encouragingly, real-world applications are starting to emerge, showing the clear value of using FL to let LLMs learn from distributed IoT data privately and effectively. However, there is still a gap between this potential and widespread, reliable use. To close this gap, the research community needs to focus on several key areas. We urgently need breakthroughs in on-device efficiency for tiny edge devices, more robust algorithms that can handle messy real-world data and potential attacks, reliable ways to guarantee privacy and fairness, standard benchmarks to measure progress fairly, and clear thinking on the legal, ethical, and economic implications. By taking on these challenges with focused, collaborative research, we can unlock the true promise of this technological convergence. Getting this right means building a future with distributed AI systems that are not only powerful and efficient but also fundamentally trustworthy and respectful of data rights, impacting critical areas from industry to healthcare and beyond. Conceptualization and methodology, H.Y., X.Y., K.W. and W.N. ; software, H.Y. ; validation, H.Y. ; formal analysis, H.Y. and X.Y. ; investigation, H.Y. and H.L. ; resources, H.Y. and H.L. ; writing--original draft preparation, H.Y. ; writing--review and editing, H.Y., H.L., X.Y., K.W., W.N., J.A.Z. and R.P.L. ; visualization, H.Y., X.Y., K.W. and W.N. ; supervision, X.Y., K.W. and W.N. ; project administration, J.A.Z. and R.P.L. ; funding acquisition, J.A.Z. and R.P.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not new data were created or analyzed in this study. The authors declare no conflicts of interest.