Abstract: The field of smart healthcare has witnessed a remarkable revolution propelled by recent advancements in communication technologies and complemented by pivotal support techniques, including artificial intelligence (AI) and the Internet of Health Things (IoHT). Traditional AI approaches, which relied on centralized data gathering and processing, have proven impractical and unattainable within the healthcare domain due to the expanding scale of healthcare domain networks and escalating privacy concerns. Addressing these challenges, federated learning (FL), an emerging distributed and collaborative technique, appears as a potential solution to resolve security and privacy concerns associated with conventional AI methodologies. By enabling the training of machine learning (ML) models on decentralized data stored across a myriad of devices, such as smartphones and IoT devices, FL schemes facilitate the analysis and interpretation of data originating from diverse wearable devices, including fitness trackers, smartwatches, and implantable healthcare devices, such as pacemakers, while upholding the privacy and security of the underlying devices and raw data. Consequently, this study focuses on elucidating the prevailing security and privacy challenges encountered when deploying FL in the context of IoHT while delving into various techniques for implementing and enhancing the security and privacy of FL within the healthcare realm. We extensively explore privacy-preserving techniques such as differential privacy, homomorphic encryption, secure multiparty computation, and other methodologies that bolster the security and privacy of FL in the healthcare sector. Furthermore, we shed light on promising avenues for future research in secure FL within the IoHT landscape. This comprehensive analysis promises valuable insights for researchers, students, and stakeholders deeply invested in this burgeoning field.