Federated Learning (FL) has emerged as a promising paradigm with the emergence of the Industrial Internet of Everything (IoE), where connected devices work together to optimize industrial processes. FL enables machine learning models to be trained across decentralized and distributed edge devices without centralizing sensitive data. FL presents a set of particular security and privacy problems that require careful study, notwithstanding its benefits in terms of data privacy and less transmission overhead. In-depth analysis of the security and privacy issues raised by the use of federated learning in the context of industrial IoE is provided in this chapter. The chapter includes a thorough analysis of the body of research, stressing the weaknesses and dangers particular to the decentralized character of FL in industrial settings. The chapter also explores the regulatory environment and compliance standards controlling data privacy in industrial sectors, offering insights into how Federated Learning systems can comply with current standards and laws. This work aims to facilitate the development and deployment of secure and privacy-preserving Federated Learning systems in industrial environments, fostering the adoption of cutting-edge machine learning techniques while safeguarding sensitive information. It does this by identifying potential threats and proposing mitigation strategies.