## 6 Conclusions  
Fall detection remains a vital component in healthcare systems, aimed at safeguarding the elderly and vulnerable populations from injury-related incidents. In this study, a robust and privacy-preserving fall-detection framework was proposed, leveraging the synergy between federated learning (FL) and transfer learning (TL) to enable efficient model training across decentralized clients, without the need to share sensitive raw data.  
The proposed methodology involved several key steps: data acquisition using a publicly available YOLO-labeled fall dataset; preprocessing, including image cropping, resizing, normalization, and binary labeling; federated learning simulation, in which data was partitioned across multiple virtual clients, simulating local training on edge devices; and federated training and aggregation, using both centralized and ring-based decentralized topologies, with FedAvg employed for model updates. Performance was validated using a held-out test set and evaluated under three randomized data splits to ensure robustness. The experimental results showed that the decentralized MobileNetV2 model outperformed all other configurations, achieving a mean test accuracy of 0.9927 and F1-score of 0.9917, and maintaining efficient computation, with an average round time of 111.17 s. These findings confirm the model's high generalizability, fast convergence, and suitability for real-world edge deployment scenarios.  
Future research directions will include rigorous evaluation of the proposed framework on publicly available benchmark datasets to enhance generalizability, the integration of robust defense mechanisms against adversarial attacks and client drift to strengthen model resilience, and the optimization of communication protocols to reduce federated learning overhead, particularly in resource-constrained deployment environments.  
Conceptualization, Q.M.H. and J.L.; methodology, Q.M.H.; software, Q.M.H.; validation, J.L. and Z.Y.; formal analysis, Q.M.H.; investigation, Q.M.H.; resources, J.L.; data curation, Q.M.H.; writing--original draft preparation, Q.M.H.; writing--review and editing, J.L. and Z.Y.; visualization, Q.M.H.; supervision, J.L.; project administration, J.L.; funding acquisition, Z.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This study adhered to ethical standards and ensured that all data used in the research was handled in compliance with relevant data privacy regulations. The fall-detection dataset used in this study is publicly available and was sourced from Kaggle, with full adherence to the licensing terms provided by the dataset creators. No personal or identifiable information was collected or processed. Additionally, all experiments were conducted in accordance with ethical research guidelines, ensuring privacy and security with respect to all data involved.  
Not applicable.  
The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request. Public sharing is restricted due to privacy and institutional policy constraints.  
The authors declare no conflict of interest.