A Privacy‐Preserving and Communication‐Efficient Federated Learning Framework for Online Education

ABSTRACT

This paper developed an adaptive federated learning framework for predicting the adaptability levels of students in an online education environment in nonindependent and identically distributed (non‐IID) conditions. In practical situations, student data is usually distributed in different multiple institutions, and because of privacy concerns, direct data sharing is often restricted. To address these challenges, the proposed method in this paper introduced an adaptive aggregation mechanism, noise‐based privacy enhancement and Top‐k model compression techniques. The adaptive aggregation strategy allocates dynamic weights to different clients based on the data size and distribution similarity to reduce the negative impact of heterogeneous data. At the same time, noise‐based privacy enhancement ensures data security, while Top‐k compression significantly reduces the communication cost during the model transmission process. Experimental results show that compared with centralized learning and FedAvg, the method proposed in this paper is competitive in terms of performance and reduces the communication cost by nearly 60%. Further analysis through confusion matrices and t‐SNE visualization shows that the method proposed in this paper improves feature separability and generates more balanced prediction results in different categories. These results suggest that this framework can effectively handle nonindependent and nonidentically distributed data and improve the generalization ability of the model. This study provides a practical and effective solution for collaborative learning in online education to protect privacy. It has potential application value in student performance prediction and personalized learning systems.