## 1 Introduction  
The Internet of Things Technology allows the construction of various intelligent information systems that can be used in critical infrastructures. Application of such systems can increase the efficiency of management of technology and business processes via solving the challenges related to automation of tasks of monitoring and managing the life cycle of equipment, energy consumption, and building optimal links between companies and service consumers [1]. This leads to the active implementation of services, applications, and information systems using this technology in different areas from smart cities and smart homes to smart grids and transport systems. However, the development of the Internet of Things is directly related to the emergence of new threats and information security risks. The landscape of such threats is extremely wide because of the variety of devices, and they can affect the security and privacy of citizens [2]. Considering [3], the risks related to the security of any Internet of Things system can be divided into the four following groups: the risks related to authentication of the devices and users; the risks related to physical impact on the system elements; the risks related to violation of confidentiality, integrity, and availability; and the risks related to the processing of personal data or other sensitive data. Security of personal data or other sensitive data in the Internet of Things is essential nowadays [4] as soon as its different components can collect and use various users' data that allows identifying them. These data incorporate biometric data, health data, etc. Moreover, according to the General Data Protection Regulation of the European Union, unique device identifiers, IP addresses, unique identifiers of mobile operators, and wireless access points are also personal data, since they help to establish the position of their owner [5]. These data are usually used in information security management systems. Thus, solving the tasks of information security management in the systems based on Internet of Things technology leads to an increase in the risks associated with the processing of personal data [6] and any unauthorized access to the information security management system threatens the confidentiality of information of IoT devices users, as well as other essential information [3]. Federated learning is a relatively novel approach to distributed machine learning. A key feature of this approach is that learning is performed locally, i.e., directly on devices that implement data collection. As the result, local models are trained and then aggregated to one global model. This feature of federated learning allows for constructing the systems that provide privacy of personal data and other sensitive data. This paper researches the applicability of federated learning for intrusion detection systems in critical infrastructures to preserve the confidentiality of data describing technological processes and equipment settings. Analysis of such data can essentially increase the efficiency of detection of complex multi-step attacks affecting the both physical level and network level of the automated process control system [7; 8]. The analysis of the research devoted to the application of the federated learning based approaches for designing intrusion detection systems showed that the researchers mainly focus on the case when all clients have datasets with a similar set of attributes. Such data distribution across clients in the federated learning settings is known as horizontally partitioned data. However, the collaborating entities could have datasets that describe the same objects but in different attributes. This case corresponds to the situation when the industrial facility consists of several collaborating entities that are responsible for different stages or parts of one technological process, and the monitoring and analysis of the state of such process require data from all entities simultaneously.