Healthcare Industry 4.0 is characterized by the assimilation of cutting-edge technologies and data-driven decision-making concerning improvement in patient care, enhance efficiency and reduce costs. There are main challenges in this era as to leverage the huge amount of healthcare data from diverse sources while ensuring patient privacy and data security. Federated Learning as an advanced machine learning technique has emerged as a promising solution to address these challenges. The model’s updates, in the form of gradients, are then sent to a central server which aggregates and updates the global model without accessing raw user data. The implementation of federated learning in a legally sound manner can help healthcare organizations harness the benefits of this technology while safeguarding patient rights and sensitive data. The legal application of advanced federated learning techniques in the healthcare industry requires a thoughtful and meticulous approach. By following legal guidelines and ensuring data privacy, healthcare organizations can leverage federated learning to advance medical research, improve patient care and drive innovation while maintaining compliance and patient trust.