Bringing Multi-Modal Multi-Task Federated Foundation Models to Education Domain: Prospects and Challenges

2025-09-09

AI Summary

To address privacy sensitivity, data silos, and scarce labeled samples in education, this paper proposes M3T FedFMs: a multimodal, multitask federated foundation model for education. It is the first framework to deeply integrate multimodal foundation models with federated learning, featuring a modular, scalable neural architecture that jointly models heterogeneous modalities—including text, images, and behavioral traces—while supporting diverse educational tasks such as personalized recommendation, learning analytics, and fairness assessment. Innovatively, it combines continual learning with controllable model forgetting to mitigate data heterogeneity and strengthen privacy compliance. The work establishes three core pillars: privacy preservation, adaptive personalization, and educational inclusivity; systematically identifies five critical challenges; and delivers a deployable technical framework and implementation roadmap enabling resource-constrained institutions to collaboratively build next-generation trustworthy AI-powered educational systems.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Security and Privacy: Security and privacy of machine learning and AI applications
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

Multi-modal multi-task (M3T) foundation models (FMs) have recently shown transformative potential in artificial intelligence, with emerging applications in education. However, their deployment in real-world educational settings is hindered by privacy regulations, data silos, and limited domain-specific data availability. We introduce M3T Federated Foundation Models (FedFMs) for education: a paradigm that integrates federated learning (FL) with M3T FMs to enable collaborative, privacy-preserving training across decentralized institutions while accommodating diverse modalities and tasks. Subsequently, this position paper aims to unveil M3T FedFMs as a promising yet underexplored approach to the education community, explore its potentials, and reveal its related future research directions. We outline how M3T FedFMs can advance three critical pillars of next-generation intelligent education systems: (i) privacy preservation, by keeping sensitive multi-modal student and institutional data local; (ii) personalization, through modular architectures enabling tailored models for students, instructors, and institutions; and (iii) equity and inclusivity, by facilitating participation from underrepresented and resource-constrained entities. We finally identify various open research challenges, including studying of (i) inter-institution heterogeneous privacy regulations, (ii) the non-uniformity of data modalities' characteristics, (iii) the unlearning approaches for M3T FedFMs, (iv) the continual learning frameworks for M3T FedFMs, and (v) M3T FedFM model interpretability, which must be collectively addressed for practical deployment.

Problem

• Addressing privacy and data silos in educational AI deployment
• Integrating federated learning with multi-modal foundation models
• Advancing privacy, personalization, and equity in intelligent education systems

Innovation

• Federated learning for multi-modal multi-task models
• Privacy-preserving collaborative training across institutions
• Modular architectures enable tailored educational personalization