Federated Learning (FL) has emerged as a promising approach for training machine learning models while preserving privacy, particularly in Internet of Things (IoT)-based environments such as healthcare. However, FL alone is insufficient for addressing all privacy challenges. This paper explores the integration of blockchain technology with FL to enhance privacy in Smart Healthcare Systems. Key contributions include a blockchain-enabled model for storage, aggregation, and gradient sharing; implementation of sidechains to improve transaction speed and reduce computational overhead; and the use of smart contracts for secure access control. The study proposes a scalable, privacy-preserving framework that aligns with healthcare regulations and supports collaborative AI applications, ultimately improving patient care and medical research.