Collaborative Edge-to-Server Inference for Vision-Language Models
📰 ArXiv cs.AI
Learn to optimize vision-language model inference by collaborating between edge devices and servers, reducing communication costs while maintaining accuracy
Action Steps
- Build a vision-language model using a framework like PyTorch or TensorFlow to understand the baseline performance
- Configure edge devices to preprocess and downsize images before transmission to the server, using techniques like resizing or compression
- Implement a collaborative inference framework that splits the model between the edge device and the server, using APIs like gRPC or HTTP to facilitate communication
- Test the framework using a dataset like COCO or Visual Genome to evaluate the trade-off between communication cost and inference accuracy
- Apply optimization techniques like quantization or pruning to further reduce the model's computational requirements and improve efficiency
Who Needs to Know This
AI engineers and researchers working on vision-language models can benefit from this collaborative edge-to-server inference framework to improve model efficiency and reduce costs. This approach can be particularly useful in applications where high-resolution images are processed, such as autonomous vehicles or smart homes.
Key Insight
💡 Collaborative edge-to-server inference can significantly reduce communication costs for vision-language models while maintaining accuracy, making it a promising approach for applications with high-resolution image processing requirements
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Optimize vision-language model inference with collaborative edge-to-server framework! Reduce communication costs while maintaining accuracy #AI #EdgeAI #ComputerVision
Full Article
Title: Collaborative Edge-to-Server Inference for Vision-Language Models
Abstract:
arXiv:2512.16349v2 Announce Type: replace-cross Abstract: We propose a collaborative edge-to-server inference framework for vision-language models (VLMs) that reduces communication cost while maintaining inference accuracy. In typical deployments, visual data captured at edge devices (clients) is transmitted to the server for VLM inference. However, transmitting full-resolution images incurs high communication cost. Conversely, aggressive downsizing or excessive compression to mitigate communica
Abstract:
arXiv:2512.16349v2 Announce Type: replace-cross Abstract: We propose a collaborative edge-to-server inference framework for vision-language models (VLMs) that reduces communication cost while maintaining inference accuracy. In typical deployments, visual data captured at edge devices (clients) is transmitted to the server for VLM inference. However, transmitting full-resolution images incurs high communication cost. Conversely, aggressive downsizing or excessive compression to mitigate communica
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