The Internet of Things has revolutionized markets by connecting previously isolated devices, but this integration raises security risks from malicious nodes that can corrupt data or disrupt operations. This evaluation of federated learning's possible application as a decentralized node identification technique highlights its advantages over standard machine learning approaches. Internet of Thing devices may collaborate on model training while protecting sensitive data and reducing network use. Federated learning and blockchain interactions create a robust framework addressing critical IoT challenges like data privacy, security, and trust. Blockchain enhances this system by providing a decentralized, tamper-resistant ledger that ensures data integrity and transparency. Automated processes, including model validation and incentive distribution, are facilitated by smart contracts. While this integrated approach improves data protection and scalability, challenges such as computational demands and consensus delays remain. The survey discusses practical applications, challenges, and future research directions for combining federated learning and blockchain in IoT systems.