## 4 Experimental Results  
In this study, all experiments were conducted using software-based tools without reliance on specific hardware equipment. The models were implemented and trained using Python 3.9 and TensorFlow 2.12, both developed by the Python Software Foundation (Wilmington, DE, USA) and Google Inc. (Mountain View, CA, USA), respectively. The development and testing were performed on systems running Ubuntu 20.04 LTS. No physical equipment requiring manufacturer details was used in the experiments. To assess the effectiveness of various deep learning models, in a federated learning context, in fall detection, this study evaluates four widely used convolutional neural network architectures--MobileNetV2, VGG16, EfficientNetB0, and ResNet50--under both centralized and decentralized (ring-based) training paradigms. Each model was trained on a custom fall-detection dataset partitioned across simulated clients to reflect real-world data distribution constraints. The evaluation focused on three key performance indicators: test accuracy, F1-score, and average training time per round, with all metrics averaged over three independent training runs to ensure statistical robustness and mitigate randomness. Among all of the models, decentralized MobileNetV2 consistently achieved the best performance, yielding the highest average test accuracy (0.9927) and F1-score (0.9917), with a modest computational cost of 111.17 s per round. Its centralized counterpart, while slightly faster (90.65 s/round), showed a lower accuracy (0.9616) and F1-score (0.9588), indicating that the decentralized strategy led to better model generalization and convergence. These results affirm the suitability of MobileNetV2's lightweight architecture for privacy-preserving and resource-efficient federated training. VGG16 also demonstrated competitive performance. The decentralized VGG16 configuration achieved an accuracy of 0.9897 and F1-score of 0.9892, marginally surpassing its centralized counterpart (accuracy: 0.9787, F1: 0.9780). However, both configurations exhibited substantially higher training times (202.68 s and 197.55 s per round, respectively), reflecting the model's computational complexity and larger parameter footprint. EfficientNetB0, while offering reduced training times compared to VGG16, displayed moderate performance in terms of accuracy and F1-score. The centralized version attained 0.8667 accuracy and a 0.8583 F1-score, while the decentralized configuration slightly improved to 0.8857 and 0.8810, respectively. These findings suggest that while EfficientNetB0 maintains a good trade-off between efficiency and accuracy, it may be less suited to federated fall-detection tasks without further optimization. ResNet50, known for its deep architecture and skip connections, achieved balanced but computationally expensive performance. The decentralized variant reported an average accuracy of 0.9093 and an F1-score of 0.9085, marginally outperforming the centralized version (accuracy: 0.8983, F1: 0.8957). However, the training time exceeded all other models, with per-round durations averaging 273.28 s (decentralized) and 247.80 s (centralized), indicating a significant computational burden. Overall, the results highlight that decentralized training generally outperforms centralized learning in terms of both predictive accuracy and F1-score across all architectures. MobileNetV2 in the decentralized configuration is especially notable for its superior accuracy, rapid convergence, and computational efficiency, making it the most effective choice for practical, privacy-aware fall-detection systems. Table 2 summarizes the quantitative results of this comparative study. While the outcomes are promising, it is important to note that the evaluation is currently limited to a single dataset. To reinforce the generalizability of these findings, future research will explore model validation on diverse public datasets and benchmark the proposed methods against established baselines in the literature.