## 2 Related Work
### Federated Learning Use Cases in IoT  
The provided table, labeled as Table 2, classifies federated learning models that aim to enhance the security of IoT devices. The work conducted in [38] presents an efficient federated defense approach known as FDA3. This study was constrained by the communication delay. The paper [39] introduced DIoT, an autonomous and self-learning distributed system that employs federated learning to detect anomalous intrusions in IoT devices. DIoT achieved a remarkable 95.6% rate of identifying devices infected with the Mirai virus in just 257 ms. The study conducted in [40] employed a ML-based IDS implemented within the framework of the IoT industry. A constraint of the study was the absence of exploration into scalability concerns. The study in [41] demonstrated the earliest research on intrusion detection in a simulated setting. This study has shown that the federated technique is highly effective in classification accuracy, computation, and communication cost when applied to the AWID intrusion detection dataset. The major downside of this methodology is the need for empirical  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Approach** & **Description** & **Privacy** \\ \hline
[33] & CNN ResNet model & Transformed network traffic data into images for DoS/DDoS detection & No \\ \hline
[34] & Radio-based RF jamming mitigation & Utilized programmable beam-steering antennas for real-time jammer categorization & No \\ \hline
[35] & UNSW-NB15 data analysis & Investigated flow, MQTT, and TCP feature clusters & No \\ \hline
[36] & Machine learning algorithms & Evaluated nine ML algorithms for IoT malware identification & No \\ \hline
[37] & Simulation-based approach & Generated artificial training data for SVM classifiers & No \\ \hline \hline \end{tabular}
\end{table}
Table 1: Security challenges in IoT attack mitigation studies.  
performance analysis. The study conducted in [42] presents MV-FLID, a FL approach for intrusion detection in IoT networks. This paper does not include a validation of the performance of FL in comparison to the ML and DL approaches. The research done in [41] employed LiM, a privacy-centric system for categorizing malware that relies on federated learning. The cloud server achieved an excellent F1 score of 95%, indicating high accuracy. However, the research needed to incorporate the convergence of the learning process, which is a drawback of the study.  
The authors in [43] evaluate the efficacy of a FL-based IDS that uses a multiclass classifier to detect various forms of assaults in IoT environments with diverse data distributions. This highlights the challenge of connecting IoT devices, indicating the need for further investigation and research. The study in [44] developed a resilient framework for securely transferring data among IoT devices. The SecureInToT model exhibited exceptional precision, with accuracy of 99.79% when binary classification was used to identify attacks. Nevertheless, its drawback lies in the requirement for testing on larger datasets. The study in [45] proposed a technique that utilizes federated learning to detect IoT devices affected by malware. This approach involves the utilization of the N-BaIoT dataset. This technique needs to address the topics of energy efficiency and learning capacity. The study in [46] presented an FL model integrating a dual-reputation reverse auction to improve security and select edge nodes in IoT settings. Their method employs a reputation-bid ratio-based algorithm for edge node selection, a flexible dropout accumulation technique to prevent malicious attacks, and blockchain for repute administration. Simulation outcomes imply that their model outperforms standard models in accuracy and reaction to assaults.