ABSTRACT
“Internet of Things” (IoT) denotes a network in which everyday objects are digitally and physically connected to one another and the outside world. Markit predicts that by the year 2030, there will be 125 billion Internet of Things devices in use worldwide. Smart homes, intelligent automation, smart cities, and cyber-physical systems are just a few of the many applications for IoT devices. Many kinds of embedded technology, including sensors, processors, and communication hardware, were used by these gadgets to gather and disperse information. As a result, artificial intelligence methods optimised for desktop computers and servers are inapplicable to IoT gadgets due to their limited hardware and software resources. Therefore, the processes to tackle these difficulties in an FL setting where they are exacerbated must be efficient and effective in terms of both resources and outcomes. ML methods, and Deep Neural Network in particular, have been shown to have interesting applications in cyber security monitoring in recent research.
