## 1 Introduction  
The development of the industrial Internet of Things (IIoT) has advanced significantly over the last few years as a result of the rapid development of wireless transmission and processing. A range of cutting-edge portable devices, such as smart phones, smart watches, and smart applications, have emerged on the IoT networks. Numerous businesses, including live gaming, smart manufacturing, navigational systems, smart cities, and smart healthcare, have extensively used these. The architecture of IoT networks still faces a number of important challenges due to their rapid proliferation. The creation of efficient and flexible control for IoT systems that can aid in energy savings, increase the number of applications, and be advantageous for potential future expansion is one of the main challenges. Along with guaranteeing security and privacy against unauthorized access, the IoT networks are cognitively demanding, time-efficient, and have a constant requirement for computing resources, which is another significant barrier. Due to the rapid development of digital technology and the growth in personal awareness, people are beginning to think about personal data security even more [1]. Distributed learning methods are needed so that devices can work together to create a single way to learn with local training. Federated learning (FL) is a decentralized platform for machine learning (ML) [2]. Unlike centralized learning frameworks, the FL framework automatically promotes confidentiality and privacy because data created on an end device does not leave the device. The data from the participating devices is used on the device itself to train the distributed learning model. A client device (e.g., a local Wi-Fi router) and a cloud server only share the settings that have been changed. Some of the benefits of using FL in wireless IoT networks are: (i) instead of exchanging huge amounts of training data, local ML system settings can save power and use less wireless bandwidth; (ii) locally calibrating the parameters of an ML model can greatly reduce transmission delay; (iii) FL can help protect data privacy because only the local learning model variables are sent and the training data stays on the edge devices. As shown in Figure 1, edge devices in FL work together to make a learning model by only sending locally learned designs to a global aggregation server and keeping the local training input at the device end [3]. The security of wireless IIoT devices is becoming an increasing concern for both manufacturers and consumers. IIoT devices are susceptible, as they lack the essential built-in security mechanisms to resist intrusions. The confined environment and low computational power of these devices are two of the primary reasons for this. The functions that can be performed on IoT devices are typically constrained by their low power consumption. Security measures subsequently keep failing as a result of that. Moreover, when individuals kept their data online without a password or with weak or default settings, the researchers uncovered one of the most critical security flaws. Frequently, IIoT devices are shipped with default, easy-to-remember passwords or no password at all. By gaining access to these devices, hackers may exploit this vulnerability with relative ease. Due to this vulnerability, attackers may exploit IIoT devices to perform serious assaults, putting the privacy of users at risk [4]. In order to prevent catastrophic harm, the research community is working on designing systems that can respond quickly and effectively to these assaults. An intrusion detection system (IDS) is a specialized security system that continuously examines network or computer system events for signs of an intrusion [5]. It examines the metadata found in network packets and employs pre-established rules to determine whether to allow or deny traffic. Intrusion detection methods can primarily be divided into two types: deployment  
Figure 1: A generalized diagram of FL for wireless IoT devices.