The incorporation of federated learning is ushering in a new era of innovation in the field of smart agriculture. The convergence of modern technologies, such as artificial intelligence (AI), the Internet of Things (IoT), and machine learning, is poised to transform agricultural practices. Smart agriculture has the potential to revolutionize farming by increasing productivity, reducing resource consumption, and improving the overall sustainability of farming practices. However, its adoption can vary based on different factors such as the level of technological infrastructure, access to resources, and the willingness of farmers to embrace new technologies. The smart agricultural industry is rising with a rapid digital transformation and this is gaining strength through the basis of cutting-edge approaches combining the machine learning, the Internet of Things (IoT), and other related technologies. This chapter investigates the role of federated learning in smart agriculture in depth, offering light on its applications and implications on sustainable development and discusses several applications based on the technologies being used in smart farming such as crop monitoring, precision farming, livestock farming, crop and soil analysis, crop forecasting, weed and pest detection, irrigation management, and harvesting. Agricultural and animal production and post-harvest applications of smart agriculture are discussed. The chapter also highlights the security vulnerabilities and the impact of challenges such as climate change. The study analyses several gaps in existing research that affect the application of IoT and machine learning in smart agriculture and suggests further investigation to improve the various aspects of smart agriculture to promote sustainable development.