Cross-cultural English vocabulary acquisition poses unique challenges due to heterogeneous learner distributions, multi-dimensional learning objectives, and data privacy constraints. Standard federated learning approaches such as FedAvg often struggle under these conditions because they assume homogeneous data distributions and single-objective optimization, leading to biased models, unstable convergence, and poor generalization across heterogeneous clients. In this paper, we propose a federated learning framework motivated by cross-cultural vocabulary learning scenarios, integrating distribution-aware client clustering, multi-objective local optimization, and privacy-aware global aggregation. The framework employs a shared semantic encoder with personalized multi-task heads and supports multi-objective optimization with dataset-specific objective instantiation, without embedding domain-specific educational modeling assumptions. We evaluate the framework on a suite of publicly available benchmarks that approximate different aspects of cross-cultural, multilingual, and heterogeneous language behavior under non-IID data distributions, rather than on real learner logs or longitudinal educational datasets. Extensive experiments show that the proposed method improves robustness-oriented metrics, reduces performance disparities among heterogeneous clients, and enhances optimization stability under privacy constraints. Ablation studies further validate the role of each framework component and provide insights into federated learning design under heterogeneous and privacy-constrained settings.
Ning Yan (Wed,) studied this question.