Federated Learning (FL) has emerged as a promising approach to training machine learning models across distributed devices while preserving data privacy by avoiding centralized data collection. However, traditional FL frameworks rely on a central server to aggregate model updates, introducing vulnerabilities such as singlepoint failures, lack of transparency, and susceptibility to adversarial attacks like model poisoning. To address these challenges, this paper proposes a decentralized AI training and inference framework that integrates blockchain technology with FL to enhance security, privacy, and trust. Our framework leverages smart contracts to automate model aggregation, decentralized storage for secure weight distribution, and cryptographic techniques such as homomorphic encryption and zeroknowledge proofs to ensure privacypreserving validation. By eliminating the need for a central authority, our approach enhances robustness against malicious actors while maintaining model accuracy comparable to traditional FL. Additionally, we introduce a consensus mechanism that verifies participant contributions, ensuring fairness and auditability. Experimental evaluations on benchmark datasets demonstrate that our framework achieves competitive performance while significantly improving privacy and resistance to attacks. This work bridges the gap between decentralized AI and federated learning, offering a scalable and secure solution for privacy-sensitive applications in healthcare, finance, and IoT. Future research directions include optimizing blockchain scalability and exploring incentive mechanisms for sustainable participation.
Federated Learning, Decentralized AI, Blockchain Technology, Privacy-Preserving Machine Learning, Smart Contracts, Homomorphic Encryption, ZeroKnowledge Proofs, Decentralized Model Aggregation, Secure Inference, Consensus Mechanism, Model Privacy, Blockchainbased Federated Learning, Distributed AI Training, Trustless AI Systems, Secure Model Sharing, Data Sovereignty, Decentralized Storage, FL Security, Blockchain Scalability, Edge Intelligence