In order to protect model updates, prevent any malicious attempts, and increase computing efficiency, this research aims to provide a highly secure, privacy-preserving, and attack-resistant federated learning (FL) framework empowered by blockchain. This gives the cause for security to organize decentralized frameworks of deep learning, uniquely denoting the vulnerabilities that come with threats associated with existing FL methods: gradient leakage, model poisoning, Byzantine errors, and blockchain scalability bounds. Traditional methods suffer from limitations in real-world applicability due to halfway methods that either put a significant load on computation or deny data privacy. This chapter proposes the design of a novel architecture in which privacy-aware FL minimizes processing and storage limitations by employing blockchain technology for model aggregation and verification. We present SecureChainFL, a hybrid dual-layer blockchain protocol developed to enhance decentralized learning's efficiency and protection. A public blockchain uses cryptographic proofs to guarantee auditability, while a private blockchain is used for model validation and aggregation. Byzantine fault-tolerant (BFT) consensus and zero-knowledge proofs (ZKP) help to secure the training process against any hostile effects, while homomorphic encryption (HE) and differential privacy (DP) help to secure model updates from privacy breaches. Our experimental results show that SecureChainFL meets the requirements of privacy-sensitive applications such as healthcare, banking, and autonomous systems because it effectively reduces privacy threats and resists model poisoning, increasing scalability. This research makes advances in privacy-preserving AI by implementing secure, effective, and attack-proof deep learning in decentralized environments.