多模态多任务（M3T）联邦基础模型用于具身AI：边缘整合的潜力与挑战
Multi-Modal Multi-Task (M3T) Federated Foundation Models for Embodied AI: Potentials and Challenges for Edge Integration
AI总结：
本文提出多模态多任务联邦基础模型（M3T-FFMs）用于具身AI，结合M3T-FMs的任务泛化能力和FL的隐私保护分布式训练，解决边缘部署中的异构具身、模态不平衡、带宽限制等挑战。
摘要：
随着具身AI系统日益多模态、个性化和交互化，它们必须从多样化的感官输入中有效学习，持续适应用户偏好，并在资源和隐私约束下安全运行。这些挑战凸显了需要能够快速适应、平衡泛化和个性化能力的机器学习模型。本文提出多模态多任务联邦基础模型（M3T-FFMs），结合M3T-FMs的任务泛化能力和FL的隐私保护分布式训练特性，实现无线边缘的智能系统。我们收集了M3T-FFMs在具身AI生态系统中的关键部署维度，统称为“EMBODY”：具身异质性、模态丰富性与不平衡、带宽和计算限制、设备持续学习、分布式控制与自主性，以及安全、隐私和个性化。针对每个维度，我们识别了具体挑战并展望了可行的研究方向。我们还提出了部署M3T-FFMs的评估框架，以及相关的权衡。最后，我们展示了M3T-FFMs的原型实现并评估了其能耗和延迟性能。
英文摘要：
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: multi-modal multi-task foundation models (M3T-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 AI environments. In this vision paper, we introduce multi-modal multi-task federated foundation models (M3T-FFMs) for embodied AI, a new paradigm that unifies the strengths of M3T-FMs with the privacy-preserving distributed training nature of FL, enabling intelligent systems at the wireless edge. We collect critical deployment dimensions of M3T-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 M3T-FFMs in embodied AI systems, along with the associated trade-offs. Finally, we present a prototype implementation of M3T-FFMs and evaluate their energy and latency performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
