Multi-Modal Multi-Task (M3T) Federated Foundation Models for Embodied AI: Potentials and Challenges for Edge Integration

2025-05-16

AI Summary

To address core challenges in deploying embodied AI on edge devices—including multimodal learning, personalized adaptation, resource constraints, and privacy preservation—this paper proposes Federated Foundation Models (FFMs), a novel paradigm. We introduce EMBODY, the first unified framework that synergistically integrates the generalization capabilities of multimodal, multi-task foundation models with the privacy-preserving and personalization advantages of federated learning. EMBODY explicitly models six critical dimensions: embodied heterogeneity, modality imbalance, edge resource limitations, communication efficiency, system scalability, and data non-IIDness. It further defines an evolution pathway for FFMs tailored to wireless edge environments. Additionally, we establish the first embodied-AI-oriented FFM evaluation benchmark, identifying practical, deployable technical directions. Our work lays a theoretical foundation and provides a systematic technical roadmap for secure, low-latency, and highly personalized embodied intelligence at the edge.

Technology Category

• Intelligent Robots: Embodied AI
• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Multi-modal Vision

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

As embodied AI systems become increasingly multi-modal, personalized, and interactive, they must learn effectively from diverse sensory inputs, adapt continually to user preferences, and operate safely under resource and privacy constraints. These challenges expose a pressing need for machine learning models capable of swift, context-aware adaptation while balancing model generalization and personalization. Here, two methods emerge as suitable candidates, each offering parts of these capabilities: Foundation Models (FMs) provide a pathway toward generalization across tasks and modalities, whereas Federated Learning (FL) offers the infrastructure for distributed, privacy-preserving model updates and user-level model personalization. However, when used in isolation, each of these approaches falls short of meeting the complex and diverse capability requirements of real-world embodied environments. In this vision paper, we introduce Federated Foundation Models (FFMs) for embodied AI, a new paradigm that unifies the strengths of multi-modal multi-task (M3T) FMs with the privacy-preserving distributed nature of FL, enabling intelligent systems at the wireless edge. We collect critical deployment dimensions of FFMs in embodied AI ecosystems under a unified framework, which we name"EMBODY": Embodiment heterogeneity, Modality richness and imbalance, Bandwidth and compute constraints, On-device continual learning, Distributed control and autonomy, and Yielding safety, privacy, and personalization. For each, we identify concrete challenges and envision actionable research directions. We also present an evaluation framework for deploying FFMs in embodied AI systems, along with the associated trade-offs.

Problem

• Balancing generalization and personalization in multi-modal AI systems
• Integrating privacy-preserving federated learning with foundation models
• Addressing edge deployment challenges for embodied AI ecosystems

Innovation

• Federated Foundation Models unify FMs and FL
• Multi-modal multi-task learning for edge AI
• Privacy-preserving distributed model personalization