## 1 Introduction  
Digital transformation has begun with the rapid rise of networked devices and smart systems across industries. IoT has become a cornerstone technology, altering how devices connect and perform in complicated ecosystems [1]. Healthcare, industrial automation,and smart cities use IoT devices for real-time data sharing and communication [2]. Modern technologies like AI, Big Data, and Cloud Computing improve IoT systems' analytical and operational capabilities, accelerating this shift [3]. Even with these advances, IoT networks' interconnectedness and pervasiveness make them vulnerable to DDoS attacks [4]. IoT networks are linked. Therefore, DDoS attacks flood targeted systems with malicious traffic, making them unavailable to legitimate users [5]. Such attacks interrupt services, cost money, and damage user confidence. IoT devices' decentralization and weak security worsen these vulnerabilities. Cheapness typically trumps security in IoT devices. They often have inadequate encryption and protection systems, making them ideal targets for attackers [6; 7]. The variability and dynamic nature of IoT networks make security framework standardization difficult, stressing the necessity for specific solutions. Federated Learning (FL) provides a possible solution to IoT security issues. FL lets devices train independently on their data and share only model updates with a central server, enabling distributed learning [8]. Decentralized systems save computing costs and improve data privacy by not transmitting raw data. FL's adaptive and scalable real-time intrusion detection and mitigation framework fits IoT networks' decentralized design. However, FL-based IDSs' efficacy depends on their capacity to handle numerous vital aspects. IoT devices create massive data streams, network traffic is dynamic, and emerging attack patterns must be detected quickly. Traditional IDSs rely on centralized designs, prone to single points of failure, and lack scalability [9]. Due to the transmission and storage of sensitive data in centralized systems, user privacy might be compromised. These constraints highlight the necessity for improved, FL-enabled IDSs that can address IoT security issues. FL with a hybrid deep-learning architecture increases IoT DDoS attack detection scalability, efficiency, and privacy. The approach captures complicated network traffic patterns using CNNs' robust feature extraction and transformer-based models' flexibility. FL trains the model using decentralized data across several devices, protecting user privacy and efficiency. Decentralization reduces data centralization concerns and enhances system response time, which diminishes DDoS attacks [10]. Integrating FL with sophisticated deep-learning algorithms fills various literature gaps. Many DDoS detection systems cannot adapt to IoT traffic's heterogeneity. Many feature selection approaches ignore network data contextual dependencies. The proposed architecture addresses these issues using a federated method to improve detection accuracy, false positives, and scalability across massive IoT networks [11]. For high-traffic situations, hybrid optimization techniques in traffic routing improve system resilience to congestion and delays, assuring ongoing service delivery. The framework presents a federated deep-learning strategy for detecting scalable and privacy-preserving IoT DDoS attacks. The system detects complicated attack patterns with minimal resource use using a hybrid model incorporating ResNet for feature extraction, GhostNet for computational efficiency, and Swin-Transformer for contextual dependencies. Federated learning avoids centralized data aggregation, protecting user privacy and reducing transmission overhead. Additionally, dynamic optimization approaches improve the framework's adaptability to diverse and developing IoT contexts.