Design and Application of Art Design Information Teaching System Based on Federated Learning

This paper presents a federation learning visualization framework using machine learning to tackle the data island issue and enhance information-based teaching systems. Leveraging machine learning, teachers understand classroom limitations and enact corresponding enhancements. The framework aims for resource sharing, enriching teaching methods, providing diverse online learning opportunities, and overcoming temporal and geographical constraints. The integrated database enables access to a wide range of materials. Results indicate a 30.26% improvement over traditional methods, demonstrating enhanced learning efficiency. Integrating online self-study and offline teaching promotes comprehensive learning outcomes and practical applications in art design education, fostering diverse teaching modes and enriching learning experiences.