The healthcare sector is quickly being transformed through digitalization into a robust opportunity and a fragile dependency. As the exchange of clinical data increasingly becomes decentralized through IoMT and cloud infrastructures, resilience and privacy become two seen challenges to accommodation in incumbent models. Much of the existing Healthcare 4.0 literature so far has either focused on data security or reliability to achieve weaker resilience in some aspect-reliance on rigid architectures is insufficient to overcome ever-changing network conditions (for cases of poor connectivity or privacy-sensitive workloads), thus we raise concerns that existing work falls short of satisfactory privacy and interoperability. We investigate and propose a resilient and privacy-preserving Healthcare 4.0 framework with federated learning with blockchain auditing is utilized along with reinforcement based adaptive control. The proposed architecture allows health care nodes to learn collaboratively and collaboratively or independently while bearing privacy implications.