“Optimizing Remote Smart Learning with Wireless Networks Using Federated Learning Algorithms”

This paper presents a remote smart learning system that combines federated learning with wireless networks. By locally training student data on distributed devices and aggregating model parameters through wireless networks, the system achieves efficient learning outcomes while ensuring data privacy protection. The use of differential privacy technology ensures the security of students’ personal data without transmitting raw data, and the system's performance is enhanced by the optimized design of Wi-Fi 6 and 5G networks. Experimental results show that the federated learning model achieved 89.7% accuracy after multiple training rounds, with network latency kept under 10ms, and bandwidth utilization improved by 25% compared to traditional centralized learning models. This study demonstrates the practical application value of federated learning systems in smart education, particularly in scenarios that require high data privacy. The research provides valuable insights for educational institutions, technology providers, and policymakers in advancing smart education and safeguarding data privacy.