Heart disease remains a leading global cause of morbidity and mortality, highlighting the critical importance of early detection and treatment. Risk factors such as high cholesterol, diabetes, obesity, and hypertension significantly increase the likelihood of developing cardiovascular illnesses. Early identification of these risks can prevent severe outcomes like heart attacks, strokes, and heart failure. Advancements in artificial intelligence (AI), data science, and communication technologies have improved the analysis of comprehensive health data, including patient demographics, medical history, lifestyle factors, and clinical test results. However, challenges persist, particularly regarding resource limitations and data privacy under regulations such as the Health Insurance Portability and Accountability Act (HIPAA).To address these issues, this paper introduces a novel framework, T-FLNET (Thread protocol Federated Learning Network). T-FLNET integrates federated learning, which enables decentralized data processing to ensure data privacy, with the Thread protocol, which enhances data security and reduces the communication latency between edge devices and the global server. This framework minimizes communication overhead while safeguarding sensitive health data. A Python-based implementation of T-FLNET was applied as a case study for heart disease prediction. The results demonstrated its effectiveness in terms of accuracy, communication latency, and privacy preservation. T-FLNET offers a secure and efficient solution for early heart disease detection, showcasing its practical applicability in managing sensitive health information.