Decentralized Data Trust System for Privacy-Preserving Federated Learning

Abstract

The increasing use of artificial intelligence (AI) in sensitive and regulated domains has raised concerns regarding data privacy, trust, governance, accountability, and fair value distribution. Although federated learning (FL) enables collaborative model training without transferring raw data, conventional FL does not inherently provide decentralized auditability, trust management, or contribution-aware incentives. In this paper, a decentralized data trust system for privacy-preserving federated learning based on a four-layer architecture integrating federated training, privacy protection, blockchain-based governance, and contribution-aware incentives is proposed. The framework uses secure aggregation and differential privacy for privacy protection, blockchain and smart contracts for verifiable governance and auditability, and an approximate Shapley value mechanism for contribution-based reward allocation. The proposed system connects participant activity, trust evaluation, contribution assessment, and reward distribution within a unified workflow. A comparative analysis with conventional FL, blockchain-enabled FL, and data marketplace approaches demonstrates the broader functional coverage of the proposed architecture across privacy, auditability, trust, incentives, and regulatory traceability. As this work is conceptual and architectural, quantitative prototype validation remains part of future work to evaluate model utility, communication overhead, scalability, and incentive-computation cost.