## 5 Discussion  
The proposed federated fall-detection framework demonstrates strong classification performance, particularly with the decentralized MobileNetV2 model, which achieves a test accuracy of 99.50% and a macro F1-score of 0.9900. These results emphasize the efficacy of integrating transfer learning with federated learning (FL) to preserve user data privacy while ensuring high predictive accuracy in distributed scenarios. The observed performance across different random seeds also affirms the stability and robustness of the proposed models. By initializing experiments with three independent seeds and applying randomized data partitioning, the results showed consistent accuracy and F1-scores across runs, supporting the model's generalizability and resistance to overfitting.  
One key dimension of our comparison involves the training time analysis across both centralized and decentralized FL configurations. Decentralized models, particularly MobileNetV2, exhibited slightly higher training times per round due to sequential weight sharing among the clients in a ring topology. For example, MobileNetV2's decentralized configuration required an average of 111.17 s/round, compared to 90.65 s/round for the centralized setup. Similarly, VGG16's decentralized configuration incurred higher computational overhead (\(\approx\)205 s/round) than its centralized version (\(\approx\)197 s/round). Despite this, the improved accuracy and generalization justify the additional cost in decentralized training, especially in privacy-sensitive domains like healthcare.  
Nevertheless, the current system has some limitations. The dataset used, while annotated and structured, is relatively small and demographically narrow, potentially limiting the model's ability to generalize across diverse elderly populations with varying physical and behavioral traits. Furthermore, the binary classification scheme (fall vs. no-fall) omits the detections of nuanced scenarios such as near-falls, hesitations, or context-dependent behaviors, which are vital in real-world monitoring applications.  
Another challenge lies in the simulated FL environment used in this study. While virtual clients provide controlled and reproducible conditions, they do not fully reflect the resource constraints and network variability of actual edge devices. Issues such as memory limitations, intermittent connectivity, and computational capacity are not adequately captured in simulations. In practice, the communication overhead, especially in decentralized topologies, can be a significant bottleneck in low-resource environments.  
Finally, although the proposed system maintains data privacy by avoiding raw data transmission, it still faces potential vulnerabilities, such as adversarial attacks, client drift, and data heterogeneity, which are common in federated healthcare ecosystems. Addressing these concerns is essential to ensure security, fairness, and trustworthiness in future real-world deployments.  
\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 \multicolumn{1}{c}{**Study**} & \multicolumn{1}{c}{**Modality and Approach**} & \multicolumn{1}{c}{**Model and Technique**} & \multicolumn{1}{c}{**Best Performance**} & \multicolumn{1}{c}{**Key Contributions**} \\ \hline
[36] & Radar-based UWB with spectrograms and transfer learning & Pre-trained VGG16 (fine-tuned) & Accuracy: 95.64\% & High accuracy on small datasets using time–frequency features and TL \\ \hline Present Work & Image-based binary fall detection via federated learning (centralized and decentralized) & Decentralized MobileNetV2 + Transfer Learning & Accuracy: 99.50\%, F1-score: 0.9900 & Integrates FL and TL with privacy-aware edge deployment; ring/server training topologies \\ \hline \hline \end{tabular}
\end{table}
Table 4: _Cont._