Blockchain-Enabled Federated Learning: A Smart Contract-Based Architecture for Decentralized AI Systems

Abstract

Artificial Intelligence (AI) development in all major fields including healthcare, finance, and intelligent infrastructure raised the need for private, transparent, and secure learning systems. Federated Learning (FL) addresses data privacy by enabling parties to cooperate to train machine learning models without sharing their raw data; however, conventional FL paradigms are based on central aggregators, which introduce vulnerabilities such as single points of failure, decreased transparency, and trust issues among the cooperating parties. This work proposes a novel theoretical framework that integrates blockchain, smart contracts, and Federated Learning to develop a fully decentralized, secure, and auditable AI training platform where smart contracts manage major processes like model aggregation, verification, and reward distribution, disentangling third-party coordination. Blockchain is used as an immutable ledger that openly keeps track of all the updates to the models and participant behavior, enhancing auditability and building trust. The design further incorporates a token- based reward system that will be used to incentivize honest behavior and discourage malicious behavior, addressing root problems of data poisoning and free- riding. This decentralized approach not only improves data confidentiality and system resilience but also promotes fairness and accountability in multi- stakeholder settings. By conceptual analysis and theoretical modeling, the paper lays the foundation for an ethical and scalable AI system that takes advantage of the strengths of Federated Learning, blockchain, and smart contracts, which is a key step towards decentralized intelligence without compromising privacy, integrity, or trust.