Orchestration of Emulator Assisted Mobile Edge Tuning for AI Foundation
  Models: A Multi-Agent Deep Reinforcement Learning Approach
Wenhan Yu Terence Jie Chua Jun Zhao
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.