## 2 Related Works  
We have highlighted the contributions and limitations of existing related works focused on FL and ML adoption in IoT networks and intrusion detection. In the most recent state of the art, there are two surveys about IoT intrusion detection [8, 9]. [PERSON] et al. [8] gave an overview of IDS that are specific to the IoT and a taxonomy to organize them. Additionally, they presented a thorough comparison of the different IDS for IoT, taking things such as installation strategy, detection mechanism, and validation strategy into account. [PERSON] et al. [9] were more concerned with enhancing IoT intrusion detection processes. They investigated the current state of the art, with a focus on IoT architecture. It provided a more comprehensive and critical analysis. In another paper, [PERSON] et al. [2] presented the use of ML to defend massive IoT devices from various attacks. This work proved ML approaches and their efficacy by evaluating hundreds of research publications. [PERSON] et al. [10] focused on ML in IoT devices. Modern communication systems generate massive amounts of data, and ML optimizes resource allocation, saves energy, and improves performance. The research studied distributed learning to improve wireless device connectivity. [PERSON] et al. discussed denial-of-service (DoS) attacks in wireless sensor networks (WSNs) [11]. DoS attacks can damage any layer of a WSN's architecture, compromising its security. Their study focused on five TCP/IP protocol levels and ML DoS solutions. In addition, they studied ML-based IDSs to solve the issue. Moreover, most conventional solutions lacked an ML-oriented approach; hence, the authors recommended MLIDS to secure TCP/IP layers. Using a dimension reduction technique and a classifier, [PERSON] et al. [12] proposed a system that detects anomalies in IoT networks. Dimension reduction was performed with the help of principal component analysis to stop the performance of fault diagnostics from getting worse and to stop real-time demand threats from complex computations. This article is lacking an example of how to calculate memory size. [PERSON] et al. [13] suggest a novel deep learning-based technique for predicting the likelihood of abnormal events utilizing footage from connected surveillance systems and alerting users to those events in an IoT environment. A deep neural network, a multi-classifier, and kernel density functions make up the suggested solution. Here, abnormal behaviors are anticipated using the Random Forest Differential Evolution with Kernel Density (RFKD) method, and any abnormal activities that are identified result in signals being delivered to IoT devices using the MQTT (Message Queuing Telemetry Transport) protocol. The work does not, however, cover the tracking and detection of multiple anomalies in living environments. [PERSON] et al. [14] carried out a study that investigated a deep learning-based IDS for distributed DoS (DDoS) attacks that is built on three models: convolutional neural networks, deep neural networks, and recurrent neural networks. The performance of each model was investigated across two classification types (binary and multiclass) using two new actual traffic datasets, CIC-DDoS2019 and TON_IoT, which include various forms of DDoS attacks. The intrusion detection approach presented by [PERSON] et al. [15] is built on a two-layer dimension reduction and two-tier classification module. This method is also reported to be able to identify User-to-Root (U2R) and Remote-to-Local (R2L) cyberattacks. Both linear discriminant analysis and component analysis have been used to minimize the number of dimensions. The Network Security Laboratory-Knowledge Discovery Dataset (NSL-KDD) dataset is used throughout the entire experiment. The two-tier classification module employs the Naive Bayes and certainty factor versions of KNN to identify anomalous behavior. An intrusion detection approach based on a two-step classification system was proposed by [PERSON] et al. [16].