Adaptive Trust‐Driven Federated Learning With Blockchain for Secure <scp>AI</scp> Healthcare Diagnostics

ABSTRACT Healthcare supply chains and IoMT‐based clinical monitoring depend on continuous data sharing but remain at risk of attacks, counterfeit drugs, and privacy breaches. Although federated learning helps protect data, it still faces challenges such as malicious clients, spoofing, and inconsistent participation. To overcome these issues, this paper introduces an Adaptive Trust‐Driven Blockchain Federated Learning (ATB‐FL) framework. The design combines behavior‐based trust assessment, blockchain‐enabled authentication, and incentive–penalty strategies within a scalable security model. This approach provides tamper‐proof traceability, real‐time participant validation, and compliance with regulatory standards while keeping sensitive data local. Using the CIC‐IoMT 2024 dataset, ATB‐FL achieved 95.1% diagnostic accuracy, reduced misclassification to under 5%, and increased blockchain throughput by over 30% compared with existing methods. The effectiveness of the framework is further illustrated through case studies on vaccine cold‐chain monitoring and remote diagnostics. Overall, ATB‐FL offers a reliable and practical foundation for building secure and privacy‐preserving healthcare systems of the future.