Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models
Deploying Vision-Language Models (VLMs) on edge devices remains challenging due to their substantial computational and memory demands, which exceed the capabilities of resource-constrained embedded platforms. Conversely, fully offloading inference to the cloud is often impractical in bandwidth-limited environments, where transmitting raw visual data introduces substantial latency overhead. While recent edge-cloud collaborative architectures attempt to partition VLM workloads across devices, they typically rely on transmitting fixed-size representations, lacking adaptability to dynamic network conditions and failing to fully exploit semantic redundancy. In this paper, we propose a progress...
| Authors | Cyril Shih-Huan Hsu, Wig Yuan-Cheng Cheng, Chrysa Papagianni |
|---|---|
| Primary category | cs.LG |
| Published | 29 April 2026 |
| Last updated at source | 11 August 2026 |
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