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The increasing connectivity of medical devices along with the growing complexity, heterogeneity and attack surface of healthcare ecosystems has lead to numerous severe cyber-attacks. This paper proposes a novel collaborative security platform for threat assessment, intelligent detection and autonomous mitigation. The solution leverages machine learning (ML) and federated learning for detecting and preventing sophisticated multi-stage attacks, as well as blockchain for supporting integrity verification and accountability to defend against advanced persistent threats. The solution uses a distributed edge approach, performing intensive computations at the edge of the network, where information is generated, to achieve real-time processing of security events. The prevention capabilities employ autonomous decision-making with optimal response strategies towards cyber-attacks and run-time adaptation; these rely on dynamic risk-based models that use real-time information about security incidents.