## 2 Related Work  
The fast spread of IoT devices and networks has improved healthcare, industrial automation, and smart cities. However, these advances present significant cybersecurity difficulties, notably in fighting against DDoS attacks. Federated-Learning (FL), Machine-Learning (ML), and hybrid deep-learning models have been used in many research studies to solve these problems. This section discusses 16 influential field publications, including their goals, methods, results, and limitations. An FL-LSTM technique was presented to identify cyberattacks in the Internet of Health Things (IoHT) [12]. The solution used embedded feature selection to reduce computational complexity and achieved 97.16% accuracy on real-world IoHT data. The research only examined LSTM networks, which may restrict their application to various IoT scenarios with varied data patterns. Another study emphasized real-time detection and privacy preservation in IoHT security using AI [13]. This work was informative but did not evaluate hybrid systems for various attack scenarios. Another study developed an FL-based IDS for IoT networks, employing differential privacy to improve security during gradient transfers [14]. The algorithm protected data privacy but struggled to identify sophisticated attack patterns due to shallow learning. An improved Probabilistic Hybrid Ensemble Model (PHEC) for decentralized IoT networks showed label noise robustness but lacked scalability for large-scale deployments [15]. We created ML-based solutions for UAV eavesdropping and GPS spoofing attacks, obtaining excellent detection accuracy [16]. However, single-network models like LSTMs were less adaptable to changing attack patterns. Hybrid models combining LSTMs and GRUs were proposed to detect anomalies in UAV networks [17]. These methods showed that dynamic IoT traffic requires more scalable and flexible solutions. To identify zero-day botnet attacks in IoT contexts, Federated Deep Learning (FDL) was created [18]. High classification accuracy and low communication overhead were obtained; however, the absence of hybrid optimization approaches hindered its resilience against complicated attack scenarios. Another research introduced FLAD, an adaptable FL model for dynamic attack profiles [19]. FLAD corrected unbalanced datasets but needed further validation in resource-constrained IoT. An FL-based NIDS for Satellite-Terrestrial Integrated Networks (STINs) optimized resource allocation and CPU usage [20]. The model's scalability to additional IoT applications is intriguing yet untested. Using FL for anomaly detection in IoT traffic, FeDIoT showed considerable accuracy gains [21]. Its relevance to real-time settings with changing traffic patterns was not explored. A collaborative FL architecture was suggested for training IDSs across enterprises while protecting data privacy [22]. This model performed well in classification but struggled with diverse data. FedeX, an FL-based anomaly detection system for industrial control systems, demonstrated high detection rates and efficient deployment on edge devices [23]. FedeX was successful. However, its dataset dependence restricted its applicability to IoT situations. Federated Sequence Learning (FSL) addresses IIoT time-series modelling difficulties while emphasizing data security [24]. Data heterogeneity was handled well; however, training needed a significant amount of computing power. Another research introduced FedAGRU, an FL-based model that uses attention processes for wireless intrusion detection, resulting in improved communication efficiency [25]. Its network setup dependence limited its application to varied IoT scenarios. FLAD aims to adapt to new attack profiles dynamically, delivering high accuracy and quick convergence [26]. It was too computationally intensive for resource-constrained IoT devices. Ref. [27] emphasized the benefits of decentralized FL-based systems over centralized ML and DL techniques for privacy protection.