Collaborative Edge-to-Server Inference for Vision-Language Models

Researchers propose a collaborative edge-to-server inference framework for vision-language models that reduces communication costs by selectively transmitting only high-entropy regions of interest rather than full-resolution images. The two-stage approach maintains inference accuracy while substantially decreasing bandwidth requirements across visual question-answering tasks.

This research addresses a fundamental infrastructure challenge in deploying vision-language models at scale. Current deployments face a critical trade-off: transmitting full-resolution images from edge devices consumes excessive bandwidth, while aggressive compression sacrifices the fine-grained visual details necessary for accurate inference. The proposed framework introduces an intelligent solution through adaptive, attention-guided region selection that dynamically identifies which image portions contain information most relevant to accurate model predictions.

The technical contribution builds on established principles of entropy-based uncertainty quantification, leveraging the VLM's internal attention mechanisms to pinpoint regions requiring detailed analysis. This approach reflects broader trends in distributed machine learning toward communication-efficient architectures, particularly important as edge computing expands across IoT, autonomous systems, and real-time video processing applications.

For infrastructure providers and cloud platforms, this research carries tangible implications. Reducing communication overhead directly decreases operational costs, latency, and network congestion—critical factors for real-time vision applications. Companies deploying VLMs at the edge could significantly improve service quality while reducing infrastructure expenses. The framework's selective retransmission strategy proves particularly valuable for bandwidth-constrained environments such as mobile networks or remote monitoring systems.

The consistent experimental validation across multiple VQA benchmarks suggests the approach generalizes effectively. Future development likely focuses on adaptive threshold tuning, integration with quantization techniques, and extension to other model architectures. The work demonstrates that intelligent architectural decisions can achieve efficiency gains comparable to full-stack optimization, making it relevant for practitioners evaluating edge-to-cloud inference pipelines.

• →Two-stage framework uses entropy thresholds to determine whether additional high-resolution image transmission is necessary.
• →Selective region-of-interest retransmission dramatically reduces bandwidth while maintaining inference accuracy across benchmarks.
• →Server leverages internal VLM attention patterns to identify which image regions contain decision-critical information.
• →Framework addresses core trade-off between communication efficiency and visual detail preservation in distributed inference.
• →Results demonstrate practical viability for edge computing deployments with latency and bandwidth constraints.