Legal Logic of AI Data Governance Based on Federated Learning: Institutional Evolution from Privacy Protection to Rights Distribution

Abstract

This study focuses on the legal logic of data governance under federated learning technology in the field of artificial intelligence (AI). Through legal reasoning and literature analysis, it delves into the importance of federated learning as a key institutional approach that balances privacy and data utilization in the face of real-world challenges such as conflicts between data silos and privacy protection, disputes over the ownership of data rights in AI training works, and compliance pressures from the EU AI Act and GDPR. Starting from a legal logic inference framework and the legal value foundation of federated learning, this study reviews existing research findings and shortcomings, and constructs a logical chain of institutional evolution from privacy protection to rights allocation. The study finds that federated learning is not merely a technical tool but also an opportunity to drive legal institutional design innovation. The institutional chain linking privacy to interest allocation constitutes a new paradigm for AI data governance. By integrating legal logical inference into technical literature and fusing the three legal logics of privacy, ownership, and benefits, this research provides an innovative institutional evolution model and operational governance recommendations for AI data governance, demonstrating significant theoretical and practical significance in interdisciplinary research.