## 4 Federated Learning for Privacy-Preserving LLM Training in IoT
### Frameworks and Benchmarks  
The practical implementation and evaluation of federated LLMs rely on specialized software frameworks and benchmarks:  
Frameworks: Libraries like FedML [92] with its FedLLM component [92], Flower [93; 94], FATE-LLM [95], and FederatedScope-LLM [96] provide infrastructure for simulating or deploying FL. Features relevant to IoT/edge include support for heterogeneous devices, PEFT methods (e.g., LoRA), various aggregation algorithms, security mechanisms (DP, secure aggregation), and sometimes specific optimizations for edge deployment (e.g., efficient client runtimes, handling intermittent connectivity). Selecting a framework depends on the specific research or deployment needs regarding scale, flexibility, supported models, and available privacy/security features.  
Benchmarks: Standardized datasets and evaluation protocols are crucial for comparing different algorithms. Efforts like FedIT [97] focus on benchmarking federated  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Approach** & **Mechanism** & **Heterogeneity** & **Efficiency** & **Trade-Offs** \\ \hline PEFT-based PFL (Prompts, Adapters, LoRA) & \begin{tabular}{c} Clients train personalized PEF components attached to a shared, frozen LLM backbone. Global aggregation might occur on these PEFT components or parts thereof [16; 61; 75; 76; 77]. \\ \end{tabular} & \begin{tabular}{c} Primarily statistical (data); some methods adapt to system heterogeneity of a system targeting PEFT complexity (e.g., heterogeneous LofRA ranks) [78; 79]. \\ \end{tabular} & \begin{tabular}{c} High communication depth \\ limited by PEFT capacity; potential for negative interference if global components are poorly aggregated [81]. \\ \end{tabular} \\ \hline Model Decomposition/Partial Training & \begin{tabular}{c} Global model is structurally divided; clients train only specific assigned layers or blocks [83; 69; 82]. \\ \end{tabular} & \begin{tabular}{c} Primarily system (computations/memory); can assign smaller parts to \\ assign smaller parts to \\ weaker clients. \\ \end{tabular} & \begin{tabular}{c} Reduced client computation; communication dependent on PEFT; \\ punching design; potential information loss \\ between components. \\ \end{tabular} \\ \hline Knowledge Distillation (KD)-based PFL & \begin{tabular}{c} Uses outputs (logits) or intermediate features from a global “teacher” model (or ensemble of client models) to guide the training of personalized local “student” models [84; 85; 86]. \\ \end{tabular} & \begin{tabular}{c} Communication \\ involves log- \\ itse/features, potentially smaller \\ than parameters; \\ client computation \\ depends on student model size. \\ \end{tabular} & \begin{tabular}{c} Disstillation process can be \\ complex; potential privacy \\ leakage from shared logits; \\ the student’s model might not perfectly capture the teacher’s knowledge. \\ \end{tabular} \\ \hline Meta-Learning-based PFL (e.g., Reptile, MAML adaptations) & \begin{tabular}{c} Learns a global model initialization that can be rapidly adapted (fine-tuned) to each client’s local data with few gradient steps [74]. \\ \end{tabular} & \begin{tabular}{c} Focuses on adapting to statistical (data) \\ heterogeneity. \\ \end{tabular} & \
\begin{tabular}{c} Communication \\ similar to standard \\ FL; potentially \\ more local computation during \\ adaptation phase. \\ \end{tabular} &
\begin{tabular}{c} Can be sensitive to task \\ diversity across clients; \\ training the meta-model \\ can be computationally \\ intensive. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative overview of personalized PFL approaches for LLMs.  
instruction tuning. FedNLP [98] provided early benchmarks for standard NLP tasks in FL. OpenFedLLM aims to offer a comprehensive platform with multiple datasets and metrics [99]. However, benchmarks specifically capturing the complexities of real-world IoT data heterogeneity, network conditions, and device constraints for LLMs are still needed.