Federated learning (FL) is a novel methodology aiming at training machine learning (ML) and deep learning (DL) models in a decentralized manner in order to solve three main problems seen in the artificial intelligence (AI) sector, namely, (a) model optimization, (b) data security and privacy, and (c) resource optimization. FL has been established as the “status quo” in today's AI applications especially in the industrial and critical infrastructure (CI) domain, as the three aforementioned pillars are invaluable in assuring their integrity. CIs include important facilities such as industrial infrastructures (smart grids, manufacturing, powerlines, etc.), medical facilities, agriculture, supply chains, and more. Deploying AI applications in these infrastructures is an arduous task that can compromise the CI's security and production procedures, requiring meticulous integration and testing. Even a slight mistake leading to the disruption of operations in these infrastructures can have dire consequences, economical, functional, and even loss of life. FL offers the needed functionalities to galvanize the integration and optimization of 96artificial intelligence in critical infrastructures. In this chapter, we will outline the application of federated learning in decentralized critical infrastructures, its advantages and disadvantages, as well as the different state-of-the-art techniques used in the Cl domain. We will showcase how the centralized ML approach transitions into the federated domain while we will show practical examples and practices of deploying the federated learning example in representative CIs, like, power production facilities, agricultural sensor networks, smart homes, and more. <!----> <!----> <!----> <!---->

A simple and accurate packet delay distribution analysis is presented for the RTS/CTS access scheme of the IEEE 802.11 distributed coordination function (DCF). The packet delay distribution is effectively studied by developing an analytical model that calculates the probability that a packet is successfully transmitted after experiencing a delay time equal to a given value. The model uses simple mathematical relations and provides accurate delay distribution curves. The accuracy of the analytical model is verified by simulations