Enhancing model performance on low-capacity devices remains a significant challenge in the Internet of Models (IoM). Inspired by the efficiency of disentangled representation learning, this study proposes a model decomposition approach to improve response time on low-capacity devices while maintaining accuracy, through collaborative learning with models deployed on higher-capacity devices. First, we introduce a divide-and-conquer strategy that decomposes and learns knowledge within each data sample. This enables lightweight sub-models, tailored to specific knowledge components, to respond efficiently on low-capacity devices in the IoM system. Second, we design a progressive crossfusion mechanism to promote mutual enhancement among these knowledge-specific sub-models. Third, we optimize the learning process by aligning the updates of these sub-models with those of the models on higher-capacity devices. This collaborative optimization is guided via knowledge distillation during model aggregation. Experimental results on image classification tasks demonstrate that our approach reduces response time by 9.87 % to 60.31 % without compromising accuracy.

By deploying distributed storage space on edge servers, mobile edge networks significantly enhance computation and transmission efficiency for wireless tasks. The selection of an appropriate caching policy not only optimizes bandwidth utilization but also alleviates network congestion. Given the intricate connectivity and vast data volume in edge computing, coupled with users’ demand for rapid response times, proposing a cache solution that closely matches data attributes becomes imperative to enhance overall efficiency. In this paper, we introduce RECG, a high-speed edge caching scheme designed specifically for graph data, leveraging the intricate data connectivity. RECG generates query graphs from edge servers to ensure swift and accurate identification of popular nodes. Additionally, we introduce a rapid hot-spot propagation partitioning technique to optimize the partitioning process, increasing the hit rate of partitioned subgraphs in the cache while reducing runtime. Our experimental evaluation, conducted on real-world datasets, compares RECG with specialized edge caching graph partitioning algorithms such as LGPE and other baseline algorithms. The thorough experimental results demonstrate the advantages of the proposed algorithm in terms of cache hit rate and processing delay.