PERFED: Privacy Preserving Personalized Federated Learning with Reinforcement and Meta-Learning for Digital Language Education

Personalized language learning in digital education faces challenges in balancing personalization, data privacy, and limited user interaction data. To mitigate these issues, this paper proposes PERFED, a Privacy-Preserving Personalized Federated Learning paradigm with Reinforcement Learning and Meta-Learning for adaptive digital language education. PERFED introduces three key innovations: (i) a privacy-preserving federated optimization mechanism with adaptive differential privacy noise that adjusts based on client data sensitivity to ensure strong privacy protection while minimizing utility loss; (ii) a reinforcement learning-based adaptive policy that dynamically selects personalized learning strategies to improve learner engagement and accelerate convergence; and (iii) a meta-learning-driven module designed for quick adaptation to new users in low-data scenarios, improving cold-start performance in personalized language learning scenarios. Experiments on the archived multilingual Duolingo Second Language Acquisition Modeling corpus show that PERFED achieves 83.3%±0.47 accuracy over five runs, outperforming FedAvg (78.5%), FedProx (80.2%), and FedPer (81.9%) under non-IID federated settings. PERFED also reduces convergence rounds while improving training stability under heterogeneous client distributions, all while maintaining a maximum cumulative privacy loss of (ϵ=4.84, δ=10−5) under a Rényi differential privacy accountant. These results demonstrate the effectiveness and robustness of PERFED for privacy-preserving intelligent language education. The evaluation is an offline synchronous simulation; the behavioral engagement signal is a log-derived proxy rather than a human-subject measure, and mobile deployment performance is not claimed.