Federated learning for blockchain technology is an emerging research area that combines the privacy-preserving, decentralized model training paradigm of federated learning (FL) with the transparency, immutability, and security features of blockchain. This integration addresses challenges in distributed learning, such as trust among participants, secure aggregation of model updates, and protection against data tampering or malicious clients. Research in this area explores blockchain-based incentive mechanisms to encourage honest participation, smart contract-based orchestration of federated learning tasks, consensus protocols for secure model aggregation, and privacy-enhancing techniques such as differential privacy and secure multi-party computation. Applications span finance, healthcare, Internet of Things (IoT), supply chain management, and edge computing, where multiple stakeholders collaborate on model training without sharing raw data. Recent studies also investigate optimizing communication efficiency, scalability, and robustness against adversarial attacks, establishing federated learning integrated with blockchain as a promising framework for secure, decentralized, and trustworthy AI systems.