## 3 LLM-Empowered IoT Architecture for Distributed Systems  
Having established the foundational concepts of IoT, LLMs, and FL in the preceding section, this section transitions to explore the architectural frameworks necessary for effectively deploying LLM-empowered IoT systems within distributed environments. We begin by outlining a general multi-tier (Cloud-Edge-Device) architecture that balances computational demands with data locality and latency requirements. Subsequently, we delve into two key operational perspectives: first, how LLMs can augment and enhance  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Technique** & **Mechanism** & **Pros** & **Cons** \\ \hline Differential Privacy & \begin{tabular}{l} Adds calibrated noise to gradients, \\ updates, or data for formal (\(\epsilon,\delta\))- \\ privacy guarantees. \\ \end{tabular} & \begin{tabular}{l} Strong, mathematical privacy guarantees; \\ ances; relatively lower computational overhead than cryptographic \\ methods. \\ \end{tabular} & \begin{tabular}{l} Direct privacy–utility trade-off (noise vs. \\ accuracy); complex privacy budget management; \\ can impact fairness; overhead can still be significant for resource-poor IoT devices. \\ \end{tabular} \\ \hline \multirow{4}{*}{\begin{tabular}{l} Homomorphic Encryption \\ decryption \\ \end{tabular} } & \begin{tabular}{l} Allows specific computations (e.g., \\ addition) on encrypted data; server \\ aggregates ciphertexts without \\ decryption. \\ \end{tabular} & \begin{tabular}{l} Strong confidentiality against the \\ server; no impact on model accuracy (utility). \\ \end{tabular} & \begin{tabular}{l} Extremely high computational overhead (en- \\ cryption, decryption, operations); significant \\ communication overhead (ciphertext size); \\ largely impractical for direct use on most \\ IoT devices. \\ \end{tabular} \\ \hline \multirow{4}{*}{\begin{tabular}{l} Secure Multi- \\ Party Computation \\ \end{tabular} } & \begin{tabular}{l} Enables joint computation (e.g., \\ sum) via cryptographic protocols \\ without parties revealing private \\ inputs. \\ \end{tabular} & \begin{tabular}{l} Strong privacy guarantees (distributed trust); no impact on model \\ accuracy. \\ \end{tabular} & \begin{tabular}{l} Requires complex multi-round interaction \\ protocols; significant communication overhead; often assumes synchronicity or fault \\ tolerance mechanisms, challenging in dynamic IoT. \\ \end{tabular} \\ \hline \multirow{4}{*}{\begin{tabular}{l} Secure Aggregation \\ \end{tabular} } & \begin{tabular}{l} Specialized protocols (often secret \\ sharing-based) optimized for securely computing the sum/average \\ of client updates. \\ \end{tabular} & \
\begin{tabular}{l} More efficient (computationally and \\ communication-wise) than general \\ HE/SMPC for the aggregation task; \\ widely adopted. \\ \end{tabular} &
\begin{tabular}{l} Protects individual updates from the \\ server during aggregation, but not the \\ final aggregated model from inference, \\ nor updates during transmission without \\ extra encryption. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of key privacy-preserving techniques in the FL context.  
the capabilities of IoT systems (termed \"LLM for IoT\") by enabling intelligent interfaces, advanced data analytics, and automated control; second, the crucial strategies and optimization techniques for efficiently running LLMs on or near resource-constrained IoT devices (termed \"LLM on IoT\"), covering essential aspects like edge fine-tuning and edge inference.