This paper proposes M3T Federated-Based Models (M3T FedFMs) that integrate federated learning (FL) to address privacy concerns in multimodal, multi-task-based models (M3T FMs), which have high potential for application in the education sector. M3T FedFMs enable collaborative and privacy-preserving learning across distributed educational institutions, accommodating diverse modalities and tasks. We argue that this will enhance three core elements of next-generation intelligent education systems: privacy protection, personalized learning, equity, and inclusion. We also suggest future research directions.
