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The efficient deployment and fine-tuning of foundation models are pivotal incontemporary artificial intelligence. In this study, we present agroundbreaking paradigm integrating Mobile Edge Computing (MEC) with foundationmodels, specifically designed to enhance local task performance on userequipment (UE). Central to our approach is the innovative Emulator-Adapterarchitecture, segmenting the foundation model into two cohesive modules. Thisdesign not only conserves computational resources but also ensures adaptabilityand fine-tuning efficiency for downstream tasks. Additionally, we introduce anadvanced resource allocation mechanism that is fine-tuned to the needs of theEmulator-Adapter structure in decentralized settings. To address the challengespresented by this system, we employ a hybrid multi-agent Deep ReinforcementLearning (DRL) strategy, adept at handling mixed discrete-continuous actionspaces, ensuring dynamic and optimal resource allocations. Our comprehensivesimulations and validations underscore the practical viability of our approach,demonstrating its robustness, efficiency, and scalability. Collectively, thiswork offers a fresh perspective on deploying foundation models and balancingcomputational efficiency with task proficiency.

Orchestration of Emulator Assisted Mobile Edge Tuning for AI Foundation Models: A Multi-Agent Deep Reinforcement Learning Approach