## 5 Conclusions  
FL is basically machine learning, where the prime objective is to tune a high-standard centralized system while data are distributed over a huge number of devices with slow network connections. This research introduces an effective FL model for real-world object recognition using designed FP-SFOA. In each round, local training is performed based on local data at every node, and the object-recognition process is performed at the training model of every node. In the training model, the input indoor image is pre-processed utilizing a bilateral filter to eliminate the calamities, and following this, object recognition is conducted employing SegNet, which is tuned by exploiting PSFOA. After, features like ResNet features, SLBT, GLCM, SIFT, SURF, ORB, and hierarchical skeleton features are extracted, and finally, object identification is performed based on FP-SFOA. Finally, the weights from every local node are aggregated at the global model using CAViaR, and then the aggregated weights are updated back to the global model. The devised FP-SFOA delivered a maximum accuracy and mean average precision of 0.950 and 0.909, whereas it gained a minimum loss function of 0.104, MSE of 0.122, RMSE of 0.035, FPR of 0.140, and communication cost of 0.078. The designed FL framework delivered superior performance and outperformed other classical models. Despite its magnificent results, some of the features during the feature extraction module consumed time to compute the feature vector, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Federated Object** & \multirow{2}{*}{**FedCV**} & \multirow{2}{*}{**DRFL**} & **Active** & **FP-SFOA-** \\  & & **Detection** & & & **Learning** & **DQN-FL** \\ \hline Computational time (sec) & 9.521478523 & 8.255785244 & 7.258746581 & 6.258744698 & 5.254789502 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Computational time. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**Time Step**} & **Metrics/ Methods** & **Federated Object Detection** & \multirow{2}{*}{**FedCV**} & \multirow{2}{*}{**DRFL**} & **Active** & **FP-SFOA-** \\  & & Accuracy & 0.816 & 0.889 & 0.880 & 0.896 & 0.950 \\  & & Loss function & 0.234 & 0.168 & 0.165 & 0.139 & 0.104 \\  & & MSE & 0.249 & 0.185 & 0.182 & 0.156 & 0.122 \\  & & RMSE & 0.050 & 0.043 & 0.043 & 0.040 & 0.035 \\  & & FPR & 0.264 & 0.201 & 0.199 & 0.173 & 0.140 \\  & & Mean average Precision & 0.761 & 0.811 & 0.839 & 0.868 & 0.909 \\  & & Communication cost & 0.148 & 0.140 & 0.111 & 0.097 & 0.078 \\ \hline \multirow{4}{*}{Dataset-2} & \multirow{4}{*}{Time Step = 200 s} & Accuracy & 0.800 & 0.828 & 0.848 & 0.883 & 0.925 \\  & & Loss function & 0.249 & 0.207 & 0.183 & 0.159 & 0.108 \\ \cline{1-1}  & & MSE & 0.264 & 0.223 & 0.200 & 0.175 & 0.125 \\ \cline{1-1}  & & RMSE & 0.049 & 0.042 & 0.042 & 0.039 & 0.034 \\ \cline{1-1}  & & FPR & 0.279 & 0.239 & 0.216 & 0.192 & 0.143 \\ \cline{1-1}  & & Mean average Precision & 0.738 & 0.792 & 0.829 & 0.860 & 0.895 \\ \cline{1-1}  & & Communication cost & 0.156 & 0.134 & 0.121 & 0.108 & 0.080 \\ \hline \hline \end{tabular}
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Table 3: Comparative discussion. which resulted in inaccurate results on blurring images. This drawback will be considered in further research. Conceptualization, P.D.S. and X.F. ; methodology, P.D.S., M.A. and A.A.; software, P.D.S., D.E.M., M.A. and A.A.; validation, X.F., P.D.S. and M.A. ; formal analysis, X.F., P.D.S. and M.A. ; investigation, P.D.S., M.A. and A.A.; resources, P.D.S., D.E.M. and X.F. ; data curation, P.D.S., M.A. and A.A.; writing--original draft preparation, P.D.S. ; writing--review and editing, P.D.S., M.A. and A.A.; visualization, P.D.S. and A.A.; supervision, X.F. ; project administration, X.F. ; funding acquisition, X.F. All authors have read and agreed to the published version of the manuscript. This research was funded by The National Natural Science Foundation of China Grant (No. 83121031). Not applicable.