Smart Cities Reimagined: Enhancing Traffic Management through Federated Multimodal Learning

With IoT devices becoming part of people’s daily lives, several industries have benefited from the equally rampant use of IoT devices to analyze data in real time. Nonetheless, significant challenges remain concerning data protection, security, and processing. This paper investigates the enhancement of FML to overcome these challenges in IoT devices. FML also allows IoT devices to learn from several information sources while keeping the information secret. Regarding the impact of quantitative performance, our study has demonstrated FML in several IoT use cases concerning statistical gains. In the case of healthcare, FML enhances the process of RPM through a $\mathbf{9 4 \%}$ accuracy, $\mathbf{9 2 \%}$ precision, $\mathbf{9 0 \%}$ recall, and an F1-score of $\mathbf{9 1 \%}$. Also, it has a Mean of Squares Error of 0.05. Regarding the traffic management intelligent city applications; the following reference measures were realized: accuracy of $89 \%$, precision of $88 \%$, recall of $87 \%$, and $F 1$ score of $\mathbf{8 7 \%. 5 \% .}$ Moreover, the mean squared error (MSE) was computed to be equal to 0.07. The environmental monitoring attained $87 \%$ of the accuracy, $85 \%$ of the precision, $84 \%$ of the Recall, and 84 for the F1-score. 5%. Also, the mean squared error (/MSE/) was anonymously estimated as 0. 08. The prediction maintenance accuracy of industrial IoT was $91 \%$, the precision rate was $90 \%$, the recall rate was $89 \%$ and the F1-score of $89 \% .5 \%$. Further, it obtained 0 as the mean squared error (MSE). 06. Anomaly detection achieved good results: accuracy equals $93 \%, \quad \mathrm{TP}=91, \quad \mathrm{FP}=9, \quad \mathrm{TN}=93$, Precision $=91 \%$, Recall ($91.5 \%$). The mean of the squared difference was 0.04.