CE-MAC: Cloud–Edge Multiagent Collaboration for Adaptive Model Pruning

Abstract

Deploying high accuracy models on resource-limited edge is essential for industrial applications, yet challenging due to the diverse and dynamic real-world environment. Cloud–edge collaboration helps by training high-capacity models in the cloud and deploying compressed models to edge nodes. However, traditional pipelines assume a fixed setting and lack the flexibility to adapt compression strategies to the heterogeneous and evolving environment. To overcome this, we propose CE-MAC, a cloud–edge collaborative large language model (LLM)-based multiagent system (LLM-MAS) for adaptive edge model compression. We adopt model pruning for its support of customizable and progressive compression, enabling flexible adaptation in dynamic edge environments. The LLM-MAS acts as the decision hub, generating device-specific pruning policies based on real-time feedback from both cloud and edge, enabling better cloud–edge co-evolution. We evaluate CE-MAC on image classification, keyword spotting, bearing fault diagnosis, and electricity theft detection tasks, achieving state-of-the-art performance across heterogeneous data and varying compression levels.