Research on blockchain technology combined with federated learning focuses on creating secure, privacy-preserving, and decentralized machine learning frameworks. In such systems, blockchain provides an immutable and transparent ledger to record model updates, track contributions from multiple participants, and ensure accountability without sharing raw data. Federated learning enables collaborative model training across distributed nodes while keeping sensitive data local, addressing privacy concerns in applications like healthcare, finance, and IoT networks. The integration of blockchain ensures trust among participants, prevents model tampering, and supports incentive mechanisms to encourage honest participation. Overall, the synergy of blockchain and federated learning enhances data security, model reliability, and collaborative intelligence in decentralized environments.