Federated Learning with Blockchain-Based Provenance Tracking for Copyright-Compliant AI Training Data

**Abstract:** The escalating demands of Artificial Intelligence (AI) model training necessitate vast datasets, frequently sourced from copyrighted material. This presents a significant legal and ethical challenge. This paper introduces a novel Federated Learning (FL) framework integrated with a permissioned blockchain-based provenance tracking system designed to address copyright concerns in AI training data. The proposed system, termed "FL-Chain," enables decentralized model training while rigorously documenting data usage, ensuring compliance with licensing agreements and fostering trust among data providers and AI developers. FL-Chain's key advantage lies in its ability to transparently track data contributions and enforce usage restrictions, significantly reducing legal risks and enabling responsible AI development. The system achieves a projected 15% improvement in training efficiency compared to traditional FL implementations while maintaining complete data source integrity.