Model Collaboration at Network Edge: Feature-Large Models for Real-Time IoT Communications

The growth of the Internet of Things (IoT) has reshaped the way devices, systems, and applications connect, leading to an enormous surge in data generation across various domains. This expansion, paired with the exponential increase in IoT devices, requires advanced data analysis capabilities to manage the multimodal sensory data collected by the massive IoT devices in real time, such as sensor outputs, visual data, audios, and videos. To address this challenge, large generative artificial intelligent (AI) models are designed, showing promise in processing multimodal data. However, deploying these models on IoT devices is constrained by limited computational power, memory, and energy resources, preventing full realization of their potential for real-time IoT systems. To address these limitations, we propose an innovative end-edge collaborative model framework between end nodes and edge servers, designed to balance computational load and optimize resource use. This approach transmits both extracted features and residual mapping data from end nodes to edge servers, allowing for spectrum efficient data handling across the network. Our work formulates an optimization strategy to enhance mean average precision (mAP) by adjusting task distribution, bandwidth, and data quantization in response to real-time network and device conditions. Comprehensive simulations demonstrate the proposed approach’s superiority over conventional centralized edge model computing and distributed end model computing frameworks, achieving enhanced efficiency across various communication rates in real time.