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.

本研究提出一种创新的范式，将移动边缘计算（MEC）与基础模型集成，旨在增强用户设备（UE）上的本地任务性能，该设计通过创新的仿真器 - 适配器架构将基础模型分成两个协同模块，既节省了计算资源又保证了适应性和下游任务的优化效率，并引入了一种精细调整的资源分配机制来满足分散式环境中的仿真器 - 适配器结构的需求。为应对该系统带来的挑战，采用了一种混合多智能体强化学习（DRL）策略，并通过全面的仿真和验证验证了我们方法的实用性，展示了其鲁棒性、效率和可扩展性，该工作为部署基础模型和平衡计算效率与任务能力提供了新视角。