## 1 Introduction
Each component contributes uniquely, where IoT provides pervasive data sources, LLMs offer powerful reasoning and language capabilities, FL supports decentralized learning, and PETs ensure data confidentiality, together forming a foundation for scalable, intelligent, and privacy-aware edge AI systems. More specifically, this review provides a comprehensive analysis of the state of the art regarding architectures, methodologies, challenges, and potential solutions for integrating IoT, LLMs, and FL, with a specific emphasis on achieving privacy-preserving intelligence in edge computing environments. We explore architectural paradigms conducive to edge deployment based on [3], investigate key enabling techniques including PEFT methods like low-rank adaptation (LoRA) [16] and distributed training strategies such as Split Federated Learning (SFL) [17; 18], and systematically analyze the inherent multifaceted challenges spanning resource constraints, communication efficiency, data/system heterogeneity, privacy/security threats, fairness, and scalability [3]. Mitigation strategies are discussed alongside critical comparisons highlighting advantages and disadvantages. We survey recent applications to illustrate practical relevance [19]. While existing surveys may cover subsets of this intersection, such as FL for IoT [20; 21] or FL for LLMs [22], this review offers a unique contribution by focusing specifically on the three-way synergy (IoT + LLM + FL) and its implications for privacy-preserving edge intelligence [10]. We aim to provide a structured taxonomy of relevant techniques, critically compare their suitability for resource-constrained and distributed IoT settings, identify research gaps specifically arising from this unique technological confluence, and propose targeted future research directions essential for advancing the field of trustworthy, decentralized AI [23]. As summarized in Figure 2, the subsequent sections are structured as follows: Section 2 introduces foundational concepts related to IoT systems, LLMs, FL principles, and PETs. Section 3 discusses architectural considerations for deploying LLMs within IoT ecosystems. Section 4 examines FL methodologies specifically adapted for LLM training and fine-tuning in this context, including frameworks and data considerations. Section 5 analyzes the  
Figure 1: Conceptual overview of technological convergence for synergistic AI solutions. This diagram illustrates the roles of key components: IoT for data provision, LLMs for intelligence, FL for privacy-preserving distributed training, and PETs for security, together forming a foundation for advanced edge AI systems. unique synergistic effects arising from the integration of IoT, LLMs, and FL, highlighting emergent capabilities. Section 6 provides an expanded analysis of key challenges encountered in the integration, discusses mitigation strategies, and evaluates inherent trade-offs. Section 7 identifies critical research gaps and elaborates on future research directions stemming from the synergistic integration. Section 8 concludes the review, summarizing the key insights and forward-looking perspectives on privacy-preserving, intelligent distributed systems enabled by IoT, LLMs, and FL. To ensure a comprehensive and systematic review, we adopted a structured literature search and selection methodology. * Search Strategy and Databases: We conducted extensive searches in prominent academic databases, including Google Scholar, IEEE Xplore, ACM Digital Library, Scopus, and ArXiv (for pre-prints). The search was performed between February 2024 and May 2025 to capture the most recent advancements.