## 5 Discussion and Conclusions  
The analysis of the research papers allowed the authors to make following conclusions. * The majority of the research papers are devoted to the design of network-based intrusion detection systems. The datasets that are used to evaluate the suggested approaches are represented either by PCAP packets or features extracted from them. Thus, while in [28] the authors used the ToN_IoT dataset, they do not consider the IoT telemetry data. Only in [52] is the MQTTset dataset that contains MQTT protocol-based communications between IoT devices used for the experiments. As it was stated in Section 3, this case corresponds to the horizontally partitioned data. The explanation of this fact is closely related to the current state of FL frameworks and libraries, the training time of the FL algorithms for vertically partitioned data, and the requirements to the computational and memory resources are extremely high. In [4], it was shown that inference time for decision trees in case of vertically partitioned data takes approximately 40 min. * The proposed architecture for an IoT-based environment is typically centralized [20; 21; 22; 28; 52]. The clients are represented either by an IoT device directly or by a smart router that collects data from a set of IoT devices. The hierarchical architecture is presented in [23; 57]. * The typical experimental scenario includes splitting one dataset across \(n\) clients in such a way that clients have datasets with different types of the attacks. In major cases, the class distribution in a client's dataset is balanced, and balancing dataset allows achieving higher results in intrusion detection efficiency. Only few works study the problem of the accuracy degradation due to imbalance in training data [22; 28; 52]. * The typical metrics that are used to evaluate the efficiency of the FL-based IDS systems are machine learning metrics that characterize the analysis model efficiency: accuracy, precision, recall, and F-measure. Only few research papers analyze the impact of the FL on the computational performance, impact on network traffic, and CPU load. Namely, in [52], the proposed FL-based algorithm is evaluated in terms of time and power consumption, and in [23], the complexity of the algorithms and the training time performance are considered. Thus, it is almost impossible to evaluate practical feasibility of the FL for intrusion detection. * Scalability is often considered a certain advantage of FL; however, only few research papers focus on this problem [23; 52; 57], and the number of clients in the experiments rarely exceeds 10. * A few works study the privacy issues in FL-based IDS, and propose novel algorithms with differential privacy mechanisms to increase the security of the data inputs. In this case, the authors focus on the evaluation of the analysis model efficiency, the issues relating to practical recommendations on how to select parameters of the differential privacy mechanisms in case the client's data imbalances are not discussed. Thus, it is possible to conclude that, despite the certain benefits of FL for intrusion detection, such IDS systems are still a subject of the theoretical research. The research has shown that almost all practical challenges that are formulated at the beginning of the article are still open. Little effort has been spent on the analysis of the bias of the clients' data; different techniques are suggested for how to model it in the conditions of the lack of real-world datasets suitable for federated learning. In future research we plan to proceed with experimental evaluation of the federated learning application for the intrusion detection in the Internet of Things considering the outlined challenges, such as the bias of the clients' data. Conceptualization, E.F. and E.N. ; methodology, E.N. ; validation, E.F.; investigation, E.F., E.N. and A.S.; writing--original draft preparation, E.F. and A.S.; writing--review and editing, E.F. and E.N. ; visualization, E.N. ; project administration, E.N. ; funding acquisition, E.N.