### Use Case: Selective Forwarding Attacks in Smart Healthcare Systems  
Smart health technologies are important for the enhancement of one's wellness and personal health [32]. The devices primarily involve in-built moisture sensors for biometric data analysis, which include heartbeat rate, sleeping habits, and activity levels. These types of devices, like remote patient monitoring systems, play a crucial role in enhancing patient results and caring for chronic conditions. Devices of the type described above typically include implanted sensors to monitor blood pressure, glucose levels, and oxygen saturation. For instance, an ECG sensor, used in remote patient monitoring systems for heart problems, may detect heart rate and any anomalies the instant they happen. By checking these crucial signs often, patients and health care providers will be able to anticipate and identify potential health risks early enough to modify treatment plans where necessary and act promptly to avert adverse outcomes. By means of such real-time information, individuals can take a more active role in maintaining their own well-being while doctors obtain suggestions on how best to provide personalized treatments. Nevertheless, the interconnection of smart healthcare systems has resulted in serious cybersecurity risks, which need to be looked into if patient safety and information confidentiality are to be ensured.  
When a scenario is considered, there could be a number of security reasons regarding unauthorized access to the IoT devices during a cyber attack. For example, compromising communication systems may involve intentionally altering dosages for prescribed drugs in such a way that it places individuals in harmful situations. Their records can be used for fraud and identity theft or to commit other illegal offenses. Also, patients' personal information can be accessed with their details, violating their basic rights. Moreover, interruptions to healthcare services induced by cyber-attacks on smart healthcare infrastructure might have far-reaching consequences, jeopardizing patient care delivery and weakening faith in digital healthcare solutions. Therefore, comprehensive cybersecurity measures are necessary to guard against such attacks and preserve the integrity and dependability of smart healthcare devices and health management. The study provides a federated learning-based detection mechanism for fighting selective forwarding attacks in IoT networks to identify and mitigate routing risks in real time, utilizing feature extraction, normalization, and model training using deep learning techniques.  
The primary aim of this research is threefold:  
1. To perform an extensive review of the literature on selective forwarding attacks and the current strategies for detecting them;
2. To create a simulation environment for assessing the effects of selective forwarding attacks on RPL-based IoT networks, and;
3. To devise a security framework incorporating federated learning to improve detection accuracy of the system.  
The paper is organized in different sections as described: Section 2 provides a detailed literature review followed by the research gaps in the existing studies. In Section 3, a detailed view of the proposed framework and methodology is presented. Section 4 details the description of the results of all the learning models and their state-of-the-art comparison and discussion. Finally, in Section 5, a brief conclusion of the study is provided along with future research directions.