## 2 Related Works
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Year** & **Reference** & **Dataset** & **Classifier/Algorithm/Framework** & **Findings** \\ \hline
2021 & [13] & HHAR & RFKD & In this work, abnormal behaviors are predicted using the RFKD method, and any abnormal activities that are identified result in signals being delivered to IoT devices using the MQTT protocol. The work does not, however, cover the tracking and detection of multiple anomalies in living environments. \\ \hline
2021 & [14] & CIC-DDoS2019, TON\_IoT & CNN, DNN, RNN & This deep learning-based IDS was proposed for cybersecurity in agriculture 4.0 and identified DDoS attacks using three ML models: CNN, DNN, and RNN. The performance of each model was investigated across two classification types (binary and multiclass) using two real traffic datasets, CIC-DDoS2019 and TON\_IoT, which include various forms of DDoS attacks. \\ \hline
2020 & [15] & NSL-KDD & Naive Bayes, KNN & This proposed IDS for anomaly detection in IoT networks is based on a two-step classification system. This method is capable of identifying cyberattacks from U2R and R2L. The two-tier classification module employs the Naive Bayes and certainty factor versions of KNN to identify anomalous behavior. \\ \hline
2018 & [16] & NSL-KDD & NSNN & An intrusion detection approach based on a two-step classification system was proposed in this paper using a negative selection algorithm and neural network. This strategy saved what little power and computing resources the end devices had by getting rid of the overhead of the training process. This work is limited to the creation of the NSNN algorithm, and currently, there is no best way to implement online learning for it. \\ \hline
2020 & [17] & MNIST & Modified CoCoA framework & This research was focused on the application of FL technology for resource optimization and incentive mechanisms in edge networks. A Stackelberg game-based methodology was proposed in order to create an FL incentive mechanism to improve the interaction between devices and edge servers. This approach is useful for big IoT networks since it enables customization of different ML computing characteristics based on the capabilities of connected devices. \\ \hline
2022 & [18] & CICIDS2017 & GRU & This FL-based IDS supposedly addresses the issues of an inadequate network intrusion detection data set and privacy protection. This technique makes it possible for numerous ISPs or other organizations to carry out joint deep learning training under the premise of preserving local data. The privacy protection of the network traffic can be solved using this method, but no real-world scenario simulation was conducted to prove its feasibility. \\ \hline
2022 & [19] & MNIST & CNN & This work investigated the problem of optimizing resources and learning performance in wireless FL systems at the same time in order to reduce communication costs and improve learning performance. A Lagrange multiplier method is used by decoupling variables, such as power variables, bandwidth variables, and transmission indicators, in order to maximize effective information flow via networks. A power and bandwidth allocation mechanism based on linear search is also established. The framework can successfully schedule clients based on wireless channel dynamics and learned model parameter features. \\ \hline
2020 & [20] & REDD & SCRAPPOR, FHMM & This paper proposed an optimized framework that considered the trade-off between LDP, data utility, and resource utilization in power IoTs. Additionally, users were divided into groups according to their level of requirement for privacy, and sensitive users received better privacy protection. This work is limited to LDP and power IoTs. \\ \hline
2021 & [21] & MQTT & RF & Maximizing the learning effectiveness of various kinds of attacks is facilitated by the multi-view ensemble learning component in this work. The FL feature efficiently aggregates profiles through the use of peer learning, as the device’s data is not shared with the server. However, they did not explore unsupervised and reinforcement ML systems, which can improve intrusion detection by detecting untrained attacks. \\ \hline \hline \end{tabular}
\end{table}