A Federated Learning Approach to Privacy-Preserving Data Analysis in Multilingual English Language Learning Platforms

The research introduces a privacy-preserving data analysis method for multilingual English language learning platforms using federated learning in Python. The method balances model efficiency and user data privacy, using the EXAMS dataset and addressing language structure and proficiency challenges through language embeddings and adaptive learning rate techniques. The novelty in encryption lies in the integration of differential privacy and secure aggregation using homomorphic encryption, ensuring individual data points and confidential model updates are safeguarded, thereby enhancing privacy protection in multilingual English language learning platforms. Privacy mechanisms, including differential privacy and secure aggregation using homomorphic encryption, safeguard individual data points and confidential model updates. Python served as a foundational language for algorithm development especially tenserflow, providing flexibility and scalability in implementing federated learning techniques for privacy-preserving data analysis in multilingual English language learning platforms. End-to-end encryption techniques, such as SSL or TLS, secure communication channels during data transmission between decentralized client devices and the central server. In terms of performance, the proposed model achieves notable accuracy on language learning tasks. In comparative analyses with existing methods, the proposed approach exhibits a superiority of 2.3% in accuracy over methods, Federated Averaging, Centralized Learning, Federated Learning with Secure Aggregation, Federated Learning with Differential Privacy, which underscores the efficacy of the federated learning in prioritizing user data privacy. The Federated Learning approach with LSTM-RNN surpasses existing methods, demonstrating remarkable improvements with an accuracy of 97.67%, precision of 96.45%, recall of 96.23%, and an impressive F1-score of 97.5%.