Federated Learning Using Blockchain for Decentralized and Enhanced secure network management driven by Artificial Intelligence

6G network management is enabled by a secure and decentralized framework that integrates Federated Learning (FL) with Blockchain technology. This framework enhances AI-based decision-making and ensures data privacy. Without relying on centralized data aggregation, this integration ensures intelligence diffusion among edge devices is seamless. Due to their dependence on centralized training of AI models and outdated cloud-based infrastructures, existing solutions suffer from privacy concerns, high latency, and security vulnerabilities. These limitations make it more difficult to dynamically optimize networks and securely transfer data in large-scale, dynamic 6G environments. To ensure decentralized model training of AI and utilize Blockchain for secure and irreversible transactions, the proposed system combines Smart Contracts with a Federated Learning Consensus Mechanism (SC-FLCM). The consensus mechanism ensures model updates without revealing raw data, makes safe data-sharing agreements automatic, and smart contracts enhance efficiency and security. 6G smart Internet of Things (IoT) services, real-time resource allocation, and secure network slicing are all great examples for the proposed method. Strong security, trust, and adaptive learning are assured over various network architectures. The system outperforms traditional cloud-based AI methodologies significantly when evaluated against them in terms of data privacy (98.7% increase), latency optimization (42.5%), and accuracy of AI models (96.2%).