Distributed Intelligent Model for Privacy and Secrecy in Preschool Education

Abstract

Mobile devices, including phones, tablets, and smartwatches, have revolutionized the way we compute and have seamlessly integrated into education systems. These versatile devices store vast amounts of valuable personal data due to their rich user interactions and advanced sensor capabilities. By harnessing the potential of this data through model training, we can greatly enhance the functionality and effectiveness of smart applications, providing educators with invaluable insights for making informed decisions. However, it is crucial to acknowledge the significant risks and responsibilities associated with handling such sensitive information. One notable breakthrough in this field is distributed machine learning, which enables improved accuracy and scalability by employing a multi-node system. This approach is particularly advantageous for processing larger input data sizes, allowing for enhanced performance and reduced errors. Moreover, it facilitates assisting individuals in making well-informed choices and effectively analyzing extensive datasets. This work introduces an advanced distributed intelligent model that leverages fully distributed machine learning techniques. Through a consensus mechanism and the exchange of gradients, we ensure the utmost integrity of private data pertaining to sports activities, education, training, and the health of preschoolers. The robust privacy and security features of this model make it an ideal solution for preschool organizations and educational institutions seeking to harness the power of machine learning while upholding the strictest standards of data privacy and security.