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The integration of healthcare and data-driven technologies offers remarkable opportunities for medical research and patient care. However, it is crucial to adhere to the ethical responsibility of protecting patient data and maintaining robust privacy standards as the primary concern. Federated learning (FL) has been recognized as a promising technique to address this issue. FL allows multiple healthcare providers or institutions to collaboratively train machine learning models without the necessity of directly exchanging sensitive patient data. Nevertheless, using conventional FL methods, which rely extensively on centralized aggregators, poses significant challenges within healthcare, including privacy vulnerabilities, regulatory compliance, and the potential for malicious exploits. In order to address the above challenges, this paper presents the Cross-Silo Federated Learning Framework with Blockchain and …