The efficient deployment and fine-tuning of foundation models are pivotal in contemporary artificial intelligence. In this study, we present a groundbreaking paradigm integrating Mobile Edge Computing (MEC) with foundation models, specifically designed to enhance local task performance on user equipment (UE). Central to our approach is the innovative Emulator-Adapter architecture, segmenting the foundation model into two cohesive modules. This design not only conserves computational resources but also ensures adaptability and fine-tuning efficiency for downstream tasks. Additionally, we introduce an advanced resource allocation mechanism that is fine-tuned to the needs of the Emulator-Adapter structure in decentralized settings. To address the challenges presented by this system, we employ a hybrid multi-agent Deep Reinforcement Learning (DRL) strategy, adept at handling mixed discrete-continuous action spaces, ensuring dynamic and optimal resource allocations. Our comprehensive simulations and validations underscore the practical viability of our approach, demonstrating its robustness, efficiency, and scalability. Collectively, this work offers a fresh perspective on deploying foundation models and balancing computational efficiency with task proficiency.

翻译：基础模型的高效部署与微调是当代人工智能的关键。本研究提出了一种开创性范式，将移动边缘计算与基础模型相融合，专门用于增强用户设备上的本地任务性能。其核心在于创新的“模拟器-适配器”架构，将基础模型分为两个协同模块。该设计不仅节省了计算资源，还确保了对下游任务的适应性与微调效率。此外，我们引入了一种高级资源分配机制，针对去中心化环境下的“模拟器-适配器”结构定制优化需求。为应对该系统带来的挑战，我们采用了一种混合多智能体深度强化学习策略，能够有效处理离散-连续混合动作空间，实现动态且最优的资源分配。全面的仿真与验证证明了该方法的实际可行性，展现了其鲁棒性、高效性与可扩展性。总体而言，本研究为部署基础模型并平衡计算效率与任务性能提供了全新视角。