## 1 Introduction
[13] focus on how mobile edge intelligence (MEI) infrastructure can support the deployment (caching, delivery, training, inference) of LLMs, emphasizing resource efficiency in mobile networks. Their core contribution lies in detailing MEI mechanisms specifically tailored for LLMs, especially in caching and delivery, within 6G. * [PERSON] et al. [14] provide a comprehensive overview of FL applied to the broad domain of IoT, covering FL fundamentals, diverse IoT applications (healthcare, smart cities, autonomous driving), architectures (CFL, HFL, DFL), a detailed FL-IoT taxonomy, and challenges like heterogeneity and resource constraints. LLMs are treated as an emerging FL trend within the IoT ecosystem. * [PERSON] et al. [15] examine the integration of LLMs as a core component of edge intelligence (EI), detailing architectures, optimization strategies (e.g., compression, caching), applications (driving, software engineering, healthcare, etc. ), and offering an extensive analysis of the security and trustworthiness aspects specific to deploying LLMs at the edge. * [PERSON] et al. [10] specifically target the intersection of FL and LLMs, providing an exhaustive review of motivations, methodologies (pre-training, fine-tuning, Parameter-Efficient Fine-Tuning (PEFT), backpropagation-free), privacy (DP, HE, SMPC), and robustness (Byzantine, poisoning, prompt attacks) within the \"Federated LLM\" paradigm, largely independent of the specific application domain (like IoT) or deployment infrastructure (like MEI). While prior reviews cover areas like edge resources for LLMs [13], FL for IoT [14], edge LLM security [15], or federated LLM methods [10], they mainly look at pairs of these technologies. This survey distinctively examines the combined power and challenges of \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Survey** & **Primary Focus** & **Key Strengths** & **Distinction from Our Work** \\ \hline [PERSON] et al. [13] & & Deep dive into edge resource optimization (compute, comms, storage); mobile network (6G); detailed edge caching/delivery for LLMs. & Focuses on infrastructure for LLMs; less depth on FL specific, security/trust, or the unique synergy of IoT + LLM + FL. Less emphasis on IoT data characteristics. & Focuses on infrastructure for LLMs; less depth on FL specific, security/trust, or the unique synergy of IoT + LLM + FL. Less emphasis on IoT data characteristics. & Focuses on infrastructure for LLMs; less depth on FL specific, security/trust, or the unique synergy of IoT + LLM + FL. Less emphasis on IoT data characteristics. \\ \hline [PERSON] et al. [14] & FL for IoT applications & Comprehensive FL principles in IoT context; detailed IoT application case studies; broad FL taxonomy for IoT. & IoT application-driven; LLMs are only one emerging aspect; less depth on LLM specifics or the challenges arising from the three-way synergy. \\ \hline [PERSON] et al. [15] & LLMs integrated into EI & Deep analysis of security and trustworthiness for LLM-based EI; covers architectures, optimization, autonomy, applications broadly. & Focuses on LLM as EI component; less depth on FL methods specifically for training LLMs on distributed IoT data. \\ \hline [PERSON] et al. [10] & Federated LLMs (FL + LLM) & Exhaustive review of FL methods for LLMs (PEFT, init, etc.); deep dive into privacy/robustness specific to federated LLMs. & Focuses narrowly on FL+LLM interaction; less emphasis on the specific IoT context (data types, device constraints) or the edge infrastructure aspects. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison with related surveys. integrating all three, including IoT, LLMs, and FL, particularly for privacy-focused intelligence at the network edge. This synergy is depicted in Figure 1. It illustrates a conceptual framework in which synergistic AI solutions emerge from the integration of IoT, LLMs, FL, and privacy-preserving techniques (PETs).