## 2 Related Work  
In recent decades, several intrusion detections studies have employed machine learning models to defend the IoT network from the attacks and threats studied in [9]. The model proposed in [10] detects automated industrial IoT attacks using a machine learning algorithm. It performs functional shape analysis to extract and represent features, enhancing the machine learning algorithm's performance during attack detection. By modelling waveforms through functional representation for feature extraction, it detects different types of IIoT attacks through a novel early detection strategy. The authors of [11] presented a novel supervised machine-learning-based IDS strategy to identify security attacks in RPL-based cyber security systems. However, conventional machine-learning-based solutions mainly focus on detecting the minimum number of attacks, and they fail to cover most attacks that occur in the RPL-IoT scenario. They are also evaluated with simple IoT datasets, which could be more effective in reflecting IoT heterogeneity. The model presented in [12] utilises a machine learning model to identify combined attacks in the IoT environment. It detects attacks against two main RPL objective functions, i.e., minimum rank with hysteresis objective function (MRHOF) and objective function zero (OF0). Such a model constructs a novel IoT dataset with optimal IoT features and attacks to efficiently detect attacks. The authors of [13] presented a DETector of rOutiNg Attacks in RPL (DETONAR) model that combines the advantages of anomaly- and signature-based IDS models to successfully detect malicious behaviour in network traffic. To handle the issue of a lack of datasets of the RPL-IoT, DETONAR introduces a routing attacks dataset for RPL (RADAR) in which more than fourteen RPL attacks are covered. However, most of the abovementioned solutions exploit a centralised means of detecting attacks, whereas the RPL-IoT is mostly distributed among many regions. Hence, it is crucial to design a novel machine-learning-based IDS solution to effectively handle IoT heterogeneity. Federated learning is a distributed learning model that learns heterogeneous IoT data by enabling various IoT services, as investigated in [14]. To provide security for IoT networks, the network intrusion detection model [15] adopts the federated learning model with joint training by deep learning to improve the accuracy of intrusion detection and protect privacy in network traffic. Furthermore, several IoT studies have employed a federated learning model to preserve privacy [16; 17; 18]. The authors of [16] proposed a supportive twin delayed DDPG (S-TD3) algorithm to authenticate and preserve the privacy of the IIoT network with the assistance of the federated transfer learning model. In addition to ensuring the privacy and security of federated transfer learning blockchains, transfer-based blockchains with an authentication mechanism enrich the security and preservation standards for IIoT applications. The IoT anomaly detection model [17] adopts federated learning with the potential advantage of privacy preservation and the design of a blockchain-based asynchronous and decentralised federated learning model. Consequently, it avoids single-point failure and ensures data integrity with improved efficiency during anomaly detection. Moreover, it applies generative adversarial networks to design a private federated learning model to optimise data utility during the training process. It preserves privacy in the decentralised IoT anomaly detection system. The IoT privacy-preserving framework [18] detects malware without sharing sensitive data through the federated learning model's training and evaluation of both the supervised and unsupervised models. It supports the privacy preservation of IoT devices such as 5G, Wi-Fi, or 25G networks during computation offloading and processing in IoT devices. The work in [19] shows that FL-based IDS models are susceptible to backdoor attacks on the IoT.