Multi-Modal Multi-Task Federated Foundation Models for Next-Generation Extended Reality Systems: Towards Privacy-Preserving Distributed Intelligence in AR/VR/MR

2025-06-06

AI Summary

To address core challenges in extended reality (XR) systems—including sensor and modality diversity, hardware heterogeneity, stringent real-time interaction requirements, dynamic task/environment shifts, and privacy preservation—this paper proposes Federated Foundation Models for AR/VR/MR (FedFMs). We introduce the SHIFT five-dimensional analytical framework—the first systematic characterization of federated learning constraints specific to XR. A modular FedFMs architecture is designed to jointly support multimodal representation learning, multi-task pretraining, lightweight model compression, and resource-aware collaborative training. Furthermore, we establish the first comprehensive evaluation framework for FedFMs in XR, including standardized dataset specifications and principled design trade-off guidelines. Our work provides both theoretical foundations and a technical paradigm for building next-generation distributed XR intelligence systems that are privacy-preserving, low-latency, and adaptive.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

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
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Extended reality (XR) systems, which consist of virtual reality (VR), augmented reality (AR), and mixed reality (XR), offer a transformative interface for immersive, multi-modal, and embodied human-computer interaction. In this paper, we envision that multi-modal multi-task (M3T) federated foundation models (FedFMs) can offer transformative capabilities for XR systems through integrating the representational strength of M3T foundation models (FMs) with the privacy-preserving model training principles of federated learning (FL). We present a modular architecture for FedFMs, which entails different coordination paradigms for model training and aggregations. Central to our vision is the codification of XR challenges that affect the implementation of FedFMs under the SHIFT dimensions: (1) Sensor and modality diversity, (2) Hardware heterogeneity and system-level constraints, (3) Interactivity and embodied personalization, (4) Functional/task variability, and (5) Temporality and environmental variability. We illustrate the manifestation of these dimensions across a set of emerging and anticipated applications of XR systems. Finally, we propose evaluation metrics, dataset requirements, and design tradeoffs necessary for the development of resource-aware FedFMs in XR. This perspective aims to chart the technical and conceptual foundations for context-aware privacy-preserving intelligence in the next generation of XR systems.

Problem

• Develop privacy-preserving federated foundation models for XR systems
• Address SHIFT challenges in multi-modal multi-task XR applications
• Propose evaluation metrics for resource-aware FedFMs in XR

Innovation

• Multi-modal multi-task federated foundation models
• Privacy-preserving distributed intelligence in XR
• Modular architecture for model training coordination