## 4 Experimental Results
Table 3 illustrates performance comparison of centralized vs. decentralized models using \(t\)-test. Decentralized federated learning consistently outperformed centralized training across all models, with statistically significant gains (\(p<0.05\)). MobileNetV2 showed the greatest improvement, achieving 0.9927 accuracy and a 0.992 F1-score, highlighting its strong efficiency and generalization. VGG16, despite its larger size, also saw notable boosts. EfficientNetB0 and ResNet50 showed modest but consistent improvements, confirming the overall advantage of decentralized learning across diverse architectures. While prior studies such as SmartFall [35] and radar-based VGG16 approaches [36] successfully employ transfer learning for fall detection, the present work distinguishes itself through its integrated use of both decentralized and centralized federated learn ing (FL) frameworks. Unlike the device-specific (smartwatch) or modality-constrained (radar-based) methods, our approach adopts a general-purpose, image-based architecture that emphasizes privacy preservation, scalability, and compatibility with edge computing environments. Although all three studies utilize transfer learning and report strong classification metrics, direct numerical comparisons remain limited due to differences in experimental datasets and modalities. Nevertheless, the proposed framework, which combines MobileNetV2 and VGG16 with federated transfer learning, achieves superior results on the target dataset--reaching 99.50% test accuracy and an F1-score of 0.9900 using decentralized MobileNetV2. As summarized in Table 4, these outcomes highlight not only the high performance of our models but also the architectural flexibility and deployment readiness of our method. In contrast to earlier efforts, the proposed system supports both ring-based and server-coordinated FL topologies, making it well suited for real-world fall-detection applications in privacy-sensitive and distributed environments. \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
**Study** & **Modality and Approach** & **Model and Technique** & **Best Performance** & **Key Contributions** \\ \hline \hline \multirow{4}{*}{[35]} & Smartwatch-based & & & Real-time, device-agnostic detection; transfer learning across wearable devices & CNN + Transfer Learning & F1-score: 93\% & Real-time, device-agnostic detection; mitigates model drift and data scarcity \\ \cline{1-1} \cline{2-6}  & & & & & model drift and data scarcity \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative analysis of transfer learning-based fall-detection methods. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Model** & **Setting** & **Accuracy (Mean)** & **F1-Score (Mean)** & **Time/Round (s)** \\ \hline MobileNetV2 & Centralized & 0.9616 & 0.9588 & 90.65 \\ \hline \hline  & Decentralized & 0.9927 & 0.9917 & 111.17 \\ \hline VGG16 & Centralized & 0.9787 & 0.9780 & 197.55 \\ \hline  & Decentralized & 0.9897 & 0.9892 & 204.68 \\ \hline EfficientNetB0 & Centralized & 0.8667 & 0.8583 & 167.38 \\ \hline  & Decentralized & 0.8857 & 0.8810 & 205.05 \\ \hline ResNet50 & Centralized & 0.8983 & 0.8957 & 247.80 \\ \hline  & Decentralized & 0.9093 & 0.9085 & 273.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Internal evaluation results of centralized and decentralized federated models on a custom dataset.