###### Abstract
A variety of fall-detection methodologies have been proposed, ranging from wearable sensor-based approaches to ambient sensing technologies. Wearable devices equipped with accelerometers and gyroscopes are widely utilized to capture dynamic motion signals associated with falls [4]. In parallel, radar-based systems, particularly those leveraging Doppler shift analysis, have emerged as a privacy-conscious alternative to video surveillance, offering non-intrusive monitoring capabilities [5]. Despite these advances, such systems face significant barriers to widespread deployment, mainly due to user discomfort, frequent recharging requirements, and difficulties in accurately differentiating falls from other routine movements [6]. Recent advances in vision-based technologies and the growing availability of affordable, high-resolution video sensors have catalyzed interest in video-based fall-detection frameworks. These systems enable continuous monitoring and detailed spatial-temporal analysis, allowing the use of large-scale video datasets to train sophisticated machine learning models [7]. Specifically, convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are two deep learning architectures that have shown increased accuracy in identifying fall patterns [8]. To improve predictive accuracy even more, ensemble learning techniques have been investigated that integrate the outputs of several classifiers [9]. In this regard, transfer learning--which involves fine-tuning pretrained models on extensive action-recognition datasets with fall-specific video data--has grown in popularity as a fall-detection method. This method maintains excellent generalization capabilities while accelerating convergence and drastically reducing the demand for huge annotated datasets [10]. In the meanwhile, federated learning presents a framework for cooperative model training across dispersed healthcare settings that protects privacy. By restricting the sharing of model updates and maintaining patient data locally, federated learning tackles important issues related to data security, privacy, and adherence to laws like the General Data Protection Regulation (GDPR) [11]. Nonetheless, video-based systems raise serious privacy issues due to the inherently sensitive nature of visual data [12]. Privacy-preserving methods like video anonymization are used to remedy this; background subtraction and face or body obfuscation are increasingly integrated to strike a balance between detection accuracy and confidentiality [13]. Legal frameworks like the GDPR mandate strict adherence to ethical data processing standards, necessitating robust protection mechanisms [14]. The interpretability of detection systems based on deep learning presents another difficulty. Conventional models frequently serve as opaque \"black boxes,\" providing little information about how decisions are made [15]. In clinical applications, in which decisions directly impact patient safety and treatment, this lack of transparency can hinder acceptance by medical professionals [16]. Thus, improving model explainability is crucial for establishing credibility, confirming model predictions, and encouraging well-informed choices in medical evaluations connected to falls [17; 18].