Next-generation mobile networks are expected to facilitate fast AI model downloading to end users. By caching models on edge servers, mobile networks can deliver models to end users with low latency, resulting in a paradigm of edge model caching. In this paper, we develop a novel model placement framework, called parameter-sharing model caching (TrimCaching). TrimCaching exploits the key observation that a wide range of AI models, such as convolutional neural networks or large language models, can share a significant proportion of parameter blocks containing reusable knowledge, thereby improving storage efficiency. To this end, we formulate a parameter-sharing model placement problem to maximize the cache hit ratio in multi-edge wireless networks by balancing the fundamental tradeoff between storage efficiency and service latency. We show that the formulated problem is a submodular maximization problem with submodular constraints, for which no polynomial-time approximation algorithm exists. To tackle this challenge, we study an important special case, where a small fixed number of parameter blocks are shared across models, which often holds in practice. In such a case, a polynomial-time algorithm with a $\left(1-ε\right)/2$-approximation guarantee is developed. Subsequently, we address the original problem for the general case by developing a greedy algorithm. Simulation results demonstrate that the proposed TrimCaching framework significantly improves the cache hit ratio compared with state-of-the-art content caching without exploiting shared parameters in AI models.

翻译：下一代移动网络有望促进向终端用户快速下载AI模型。通过在边缘服务器上缓存模型，移动网络能够以低延迟将模型交付给终端用户，从而形成边缘模型缓存的范式。本文提出了一种新颖的模型放置框架，称为参数共享模型缓存（TrimCaching）。TrimCaching利用了关键观察：广泛的AI模型（例如卷积神经网络或大型语言模型）可以共享包含可复用知识的参数块的显著比例，从而提升存储效率。为此，我们构建了一个参数共享模型放置问题，旨在通过平衡存储效率与服务延迟之间的基本权衡，最大化多边缘无线网络中的缓存命中率。我们证明该问题是一个带有子模约束的子模最大化问题，对此不存在多项式时间近似算法。为应对这一挑战，我们研究了一个重要的特例，即模型中共享少量固定数量的参数块（这在实际中经常成立）。在此情况下，我们开发了一种具有$(1-ε)/2$近似保证的多项式时间算法。随后，我们通过提出一种贪婪算法来解决一般情况下的原问题。仿真结果表明，与未利用AI模型共享参数的最先进内容缓存相比，所提出的TrimCaching框架显著提升了缓存命中率。