## 5 Conclusions and Future Directions  
The study proves the effectiveness of FL in solving complex problems in IoT security. Different ML algorithms, including KNN, LR, SVM, NB, and FL-DSEA, have shown their capacity to detect routing vulnerabilities effectively. All performance parameters, including accuracy, precision, recall and F1 score, are observed at consistently high levels for varied \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Ref.** & **Dataset** & **Scalability Privacy** & **Model Used** & **Attack** & **Findings** \\ \hline \multirow{5}{*}{\begin{tabular}{} \end{tabular} } & \multirow{5}{*}{IRAD} & \multirow{5}{*}{Yes} & \multirow{5}{*}{Yes} & FL-DSEA (LR, KNN, SVM, NB) & \begin{tabular}{} \end{tabular} & SVM: Accuracy = 95\%, \\  & & & & Precision = 94\%, Recall = 97\%, F1 score = 96\% NB: \\  & & & & Accuracy = 89\%, Precision = 99\%, Recall = 79\%, F1 score = 88\% KNN: Accuracy = 95\%, Precision = 95\%, Recall = 95\%, F1 score = 95\% LR: Accuracy = 94\%, Precision = 92\%, Recall = 96\%, F1 score = 94\% & Accuracy = 98\%, Precision = 97\%, Recall = 98\%, F1 score = 97\% \\ \hline \multirow{2}{*}{[54]} & \multirow{2}{*}{IRAD} & \multirow{2}{*}{No} & \multirow{2}{*}{No} & LR, KNN, SVM, NB, MLP & \begin{tabular}{} \end{tabular} & Accuracy = 98\%, Precision = 97\%, Recall = 98\%, F1 score = 97\% \\ \hline \multirow{2}{*}{[55]} & \multirow{2}{*}{IRAD} & \multirow{2}{*}{No} & \multirow{2}{*}{No} & LR, KNN, SVM, NB, MLP & \begin{tabular}{} \end{tabular} & Accuracy = 97.14\%, Precision = 97.03\%, Recall = 96.39\%, F1 score = 96.39\% \\ \hline \multirow{2}{*}{[56]} & \multirow{2}{*}{RPL-LoBT network (self-constructed)} & \multirow{2}{*}{No} & \multirow{2}{*}{No} & IoBTSec-RPL & \begin{tabular}{} \end{tabular} & Accuracy = 98.1\%, Precision = 98.46\%, Recall = 98.1\%, F1 score = 96.00\% \\ \hline \multirow{2}{*}{[57]} & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{No} & \multirow{2}{*}{No} & ProSenAD & \begin{tabular}{} \end{tabular} & Accuracy = 97.14\%, Precision = 97.03\%, Recall = 96.39\%, F1 score = 96.39\% \\ \hline \multirow{2}{*}{[58]} & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{No} & \multirow{2}{*}{No} & IoBTSec-RPL & \begin{tabular}{} \end{tabular} & Accuracy = 98.1\%, Precision = 98.46\%, Recall = 98.1\%, F1 score = 96.00\% \\ \hline \multirow{2}{*}{[59]} & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{No} & \multirow{2}{*}{No} & ProSenAD & \begin{tabular}{} \end{tabular} & Accuracy = 0.98\%, Precision = 99\%, Precision = 98.1\%, F1 score = 98\% \\ \hline \multirow{2}{*}{[58]} & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{Yes} & \multirow{2}{*}{Yes} & FL-DSEA & \
\begin{tabular}{} \end{tabular} & Accuracy = 0.94\% \\ \hline \multirow{2}{*}{[59]} & \multirow{2}{*}{CIC-IDS-2017} & \multirow{2}{*}{Yes} & \multirow{2}{*}{Yes} & FL-SCNN-Bi-LSTM model &
\begin{tabular}{} \end{tabular} & Accuracy = 0.99\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: State of the Art Comparison. models, reflecting their capability to distinctly identify normal from harmful network behavior. The SVM and KNN models are notable for their exceptional performance, regularly reaching accuracy rates of approximately 95% on both the test and validation datasets. Moreover, upon comparing federated learning with conventional machine learning techniques, it becomes apparent that it has several distinct advantages, such as enhanced data confidentiality, distributed processing, and expandability. The results highlight the substantial influence that federated learning can have on improving the security of IoT deployments. This technology enables the development of more effective intrusion detection algorithms to tackle rising cyber threats. The performance findings of the FL-DSA model also demonstrated better detection efficiency. This solution requires the modification of the firmware of IoT devices; however, its computational expense remains modest.