## 2 Related Work
To discuss and rectify such a problem, it presents a novel data poisoning attack in which a malicious intruder implants a backdoor of aggregated information in the detection model, leading to incorrect classification of malicious and benign information. Thus, the malicious intruder can gradually poison the detection strategy by only exploiting the compromised IoT devices to inject an amount of malicious traffic. Moreover, it motivates researchers to innovate novel FL IDS models for the IoT to optimise performance. The authors of [20] introduced a defence model of FL against gradient-based reconstruction attacks in resource-limited IoT environments. Such amodel compares analyses of the loss of the training model and designs an adaptive communication mechanism by adjusting the frequency and parameter compression, resulting in a high-security achievement. The framework presented in [21] advocates a personalised federated learning model to investigate the heterogeneous issues associated with the IoT environment. It also accomplishes personalisation and maximises the performance of intelligent IoT devices by integrating four different federated learning methods: federated transfer learning, federated meta-learning, federated multitask learning, and federated distillation. The federated machine-learning-based IDS model proposed in [22] trains the models of IoT devices based on local data and maintains the security of sensitive IoT information. Furthermore, federated-learning-based server aggregation with local models maximises the detection efficiency of clients. The authors of [23] proposed a federated-learning-based attention-gated recurrent unit (FedAGRU) model to assure security at the edges of wireless networks. FedAGRU updates ubiquitous learning without sharing clients' basic information to the server and optimises the learning model. Furthermore, it neglects unimportant parameter transmissions to the server with the help of an attention mechanism and greatly enhances the security performance with minimum communication overhead. The authors of [24] presented a federated-learning-assisted intelligent IDS model that exploits the FedACNN algorithm to detect intrusions in edge-enabled industrial IoT environments. It solves the issues associated with centralised machine learning models by transferring model parameters to the global server with high security instead of sending raw data. It considerably improves IDS detection accuracy by achieving an accelerated global model using the federated learning concept. The authors of [25] proposed an ensemble multiview federated-learning-based intrusion detection model called MV-FLID. It views IoT data traces differently and forms an effective decentralised learning process with the help of federated learning. Furthermore, MV-FLID identifies and classifies intrusions and provides strong security against attacks. It improves learning efficiency by viewing the data of diverse attack classes in multiple aspects. The authors of [26] presented a three-layer FL architecture for an industrial IoT environment. In this model, the gateways are locally trained with a deep neural network (DNN) model and optimise the learning process by globally federating the locally trained models. Transfer learning models have recently attracted significant attention in detecting intrusions in RPL-based resource-constrained IoT networks by transferring knowledge from the source domain to the target domain. Another study [27] investigated the role of transfer learning in transferring knowledge for intrusion detection for new devices and transferring knowledge to detect new attacks. The model proposed in [28] uses the advantages of federated and transfer learning to improve the generalisation training efficiency of the detection system in an industrial IoT environment.