Intelligent Monitoring and Analysis of Sports Data in the Context of Rural Revitalization Based on Federated Learning

With the advancement of the rural revitalization strategy, leveraging intelligent technologies to improve the efficiency of monitoring and analyzing rural sports data has become a hot topic of research. This study proposes an intelligent monitoring and analysis framework for sports data based on federated learning, tailored for the rural revitalization context to address challenges related to data privacy protection and data silos. First, a distributed architecture for sports data analysis is designed, enabling joint analysis without aggregating data from various regions, thereby ensuring data privacy. Second, the study provides an in -depth analysis of federated learning's privacy protection capabilities, model convergence, and computational efficiency, demonstrating the advantages of this approach. Finally, experiments conducted on real-world rural sports datasets validate the proposed framework through comparative analyses, highlighting its accuracy and efficiency. Results show that the federated learning-based method offers significant advantages in data protection, real-time monitoring, and model performance.