Modality-Decoupled Federated Learning for Privacy-Preserving Embodied Intelligence in 6G
Sixth-generation (6G) wireless networks are expected to provide a key infrastructure for large-scale embodied intelligence, where heterogeneous robots collaborate through low-latency connectivity, edge intelligence, and distributed sensing. Vision-language-action (VLA) models offer a foundation by integrating visual perception, language understanding, and action generation into a unified closed-loop policy. However, training and adapting VLA models to distributed robotic agents introduce challenges in privacy protection, communication efficiency, and model heterogeneity. Existing federated learning (FL) methods overlook the intrinsic differences among vision, language, and action pathways...
| Authors | Zhuodong Liu, Xiangyu Li, Chunhong Yuan et al. |
|---|---|
| Primary category | eess.SP |
| Published | 9 September 2026 |
| Last updated at source | 9 September 2026 |
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