A DDoS attack mainly involves various online connected devices (often named a botnet) which aim to overwhelm a target with fake traffic. The DDoS attack has advanced to a low-rate traffic structure from traditional high-rate attack transmission. This transformation in DDoS attacks has introduced a new variant of attack named low-rate DDoS (LR-DDoS) [6; 7]. LR-DDoS attacks mainly focus on TCP's congestion-control vulnerability by transmitting the malicious traffic at a low constant rate lowering throughput by making use of the re-transmission time-out mechanism. Attackers launch such types of attacks from smartphones using various tools such as Slow Droid [8] and exploit the application layer weaknesses via various application layer protocols e.g., Slow Next [9]. Such attacks spread quickly and massively damage the SDN controller, resulting in unresponsive to the IoT network, and the remedial procedure is extremely difficult to perform in real-time. It is essential to secure the SDN controller from such malicious attacks so that SDN can facilitate and manage the IoT network efficiently [10]. A plethora of state-of-the-art mathematical and machine learning (ML) based LR-DDoS detection techniques have been developed, which have shown remarkable results. The former has shown better performance in terms of good detection rate, while the latter detects the LR-DDoS accurately with higher time complexity. In conventional ML-based detection schemes, the data gathered by the end hosts are usually kept on the cloud and utilized in the model training process and the trained model is further utilized for classification and prediction purposes. Although ML-based LR-DDoS detecting algorithms have shown high intrusion detection accuracy, these schemes give rise to several research questions. The huge amount of generated data by the consumer's devices and their transmission towards the centralized compute station (i.e., Cloud) can cause increased transmission delays, latency, and bandwidth consumption. In addition, the confidentiality of end-user data is a big question regarding data security and privacy [11; 12; 13]. To tackle the aforementioned issues, Google introduced Federated learning (also known as collaborative learning) that mainly trains the ML algorithms on local devices (handheld devices and/or Edge servers) without exchanging any data, thus effectively handling the issues such as data privacy, data security, data access rights and access to heterogeneous data [14]. Various FL especially distributed FL-based LR-DDoS detection techniques [15; 16] have been proposed, showing remarkable results. Authors in [15] adopted distributed federating learning and proposed an intrusion detection mechanism in a vehicular environment where the participating vehicles perform local training on collected data and share the trained model weights with the federated server, where the locally trained weights are aggregated to develop a global model. The proposed work allocates equal preference to all the locally trained model weights, reducing the overall efficiency of a final global model. Authorsin [16] proposed the Federated learning-based chained anomaly detection system, which assigns an average preference mechanism to prioritize the received model weights from different participants. The proposed scheme shows high performance compared with the [15]; however, assigning average preference may never produce the optimal global model and it is highly needed to assign the optimized preferences to each the locally trained model. Taking the aforementioned challenges into consideration, this work proposes a Weighted Federated learning-based LR-DDoS detection mechanism for SDN controllers that not only considers the benefits of Federated learning but also assigns appropriate preference to the received locally trained model weights. Meaning, the received locally trained model with high accuracy is assigned a high preference compared with the low accuracy models.