## 3 Methodology  
The proposed fall-detection framework leverages federated learning combined with deep learning architectures to ensure accurate and privacy-conscious recognition of human falls. As illustrated in Figure 1, the pipeline begins with data acquisition, for which a publicly available fall-detection dataset is employed. The raw data consists of video frames annotated in YOLO format. These annotations are parsed to extract human-centered image crops, which are then resized, normalized, and binarized into two classes: fall and no-fall. To enable fair training and testing, the full dataset is stratified into training, validation, and test splits following an 80/10/10 ratio. In the preprocessing phase, the training set is divided among multiple virtual clients, simulating a federated learning environment in which data remains local and confidential. Each client represents a decentralized node that independently holds its portion of the dataset without sharing raw data, aligning with privacy preservation principles. The federated learning stage adopts two distinct training strategies: a centralized federated learning model and a decentralized ring-based FL topology. All clients use the same base architecture initialized with pretrained ImageNet weights. The models explored include MobileNetV2, VGG16, EfficientNetB0, and ResNet50--each fine-tuned using a transfer learning approach. Custom classification layers are appended on top, while backbone layers remain frozen to retain learned visual representations. The FederatedClientUpdate() method is employed for local training, after which either the central server calls \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref** & **Dataset** & **Classification** & **Model** & **Env.** & **Key** & **Results** & **Limitation (vs. Our Work)** \\ \hline \multirow{3}{*}{[21]} & Live video streams & YOLOv3 + posture-based feature fusion & YOLOv3 + posture & Day/low light & Combines YOLO detection with posture tracking & 92\% (day), 60\% (low light) & Poor low-light accuracy; no data distribution or privacy mechanisms \\ \hline \multirow{3}{*}{[22]} & 1691 fall/1731 normal images & Tiny YOLOv4 + inference accelerator & Tiny YOLOv4 + OpenVINO & Edge device & Real-time deployment on Raspberry Pi with high precision and FPS 26 & Precision: 95\%, Reliability 96\%, reliability \\ \hline \multirow{3}{*}{[10]} & \multirow{3}{*}{21,499 images} & Attention-augmented YOLOv5 + YOLO architecture & YOLOv5 + CBAM + SE & Surveillance & Enhanced YOLOv5 with CBAM, SE, and Swish activation for feature enhancement & Accuracy: 97.3\% & High dependence on labeled data; no distributed training \\ \hline \multirow{3}{*}{[23]} & \multirow{3}{*}{YOLOv8n vs. datasets} & YOLOv8n vs. YOLOv8n vs. (compact CNNs) & Mixed scenes & Comparison of YOLOv8n & YOLOv8n better on YOLOv8n & No privacy, personalization, or federated learning \\ \hline \multirow{3}{*}{[24]} & Raspberry Pi test set & YOLOv8n with classical classifier & YOLOv8n-r1 + SVM & Embedded system & Hybrid approach on Pi4 balancing accuracy and efficiency & Balanced metrics & No real-time tracking; lacks federated learning strategy \\ \hline \multirow{3}{*}{[25]} & Custom labeled data & YOLOv5 MU + self-attention module & YOLOv5 MU + attention & Elder care & Self-attention + multiscale anchors + enhanced loss & Only centralized, cross-device adaptation or privacy-preserving design \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cont. Server Aggregation() to compute the weighted average (FedAvg), or clients sequentially exchange model updates in the ring-based topology using unweighted averaging. The Federated Learning Loop continues for R global communication rounds. In the centralized scenario, the server aggregates model updates; in the ring-based setting, no central server is required--clients perform local updates and pass the model in a chain. Both schemes allow the global model to progressively improve while respecting data locality.