Foundation models (FMs) such as GPT-4 exhibit exceptional generative capabilities across diverse downstream tasks through fine-tuning. Split Federated Learning (SFL) facilitates privacy-preserving FM fine-tuning on resource-constrained local devices by offloading partial FM computations to edge servers, enabling device-edge synergistic fine-tuning. Practical edge networks often host multiple SFL tenants to support diversified downstream tasks. However, existing research primarily focuses on single-tenant SFL scenarios, and lacks tailored incentive mechanisms for multi-tenant settings, which are essential to effectively coordinate self-interested local devices for participation in various downstream tasks, ensuring that each SFL tenant's distinct FM fine-tuning requirements (e.g., FM types, performance targets, and fine-tuning deadlines) are met. To address this gap, we propose a novel Price-Incentive Mechanism (PRINCE) that guides multiple SFL tenants to offer strategic price incentives, which solicit high-quality device participation for efficient FM fine-tuning. Specifically, we first develop a bias-resilient global SFL model aggregation scheme to eliminate model biases caused by independent device participation. We then derive a rigorous SFL convergence bound to evaluate the contributions of heterogeneous devices to FM performance improvements, guiding the incentive strategies of SFL tenants. Furthermore, we model inter-tenant device competition as a congestion game for Stackelberg equilibrium (SE) analysis, deriving each SFL tenant's optimal incentive strategy. Extensive simulations involving four representative SFL tenant types (ViT, BERT, Whisper, and LLaMA) across diverse data modalities (text, images, and audio) demonstrate that PRINCE accelerates FM fine-tuning by up to 3.07x compared to state-of-the-art approaches, while consistently meeting fine-tuning performance targets.

翻译：基础模型（如GPT-4）通过微调在多样化下游任务中展现出卓越的生成能力。分割联邦学习通过将部分基础模型计算卸载至边缘服务器，支持资源受限的本地设备进行隐私保护的基础模型微调，实现设备-边缘协同微调。实际边缘网络通常部署多个分割联邦学习租户以支持多样化的下游任务。然而，现有研究主要集中于单租户分割联邦学习场景，缺乏针对多租户环境的定制化激励机制，而这种机制对于有效协调自利的本地设备参与不同下游任务、确保满足每个分割联邦学习租户独特的基础模型微调需求（如模型类型、性能目标和微调截止期限）至关重要。为填补这一空白，本文提出一种新颖的价格激励机制，引导多个分割联邦学习租户提供战略性价格激励，从而吸引高质量设备参与以实现高效的基础模型微调。具体而言，我们首先设计了一种抗偏差的全局分割联邦学习模型聚合方案，以消除因设备独立参与导致的模型偏差。随后推导出严格的分割联邦学习收敛边界，用于评估异构设备对基础模型性能提升的贡献，从而指导分割联邦学习租户的激励策略。进一步地，我们将租户间的设备竞争建模为拥堵博弈进行斯塔克尔伯格均衡分析，推导出每个分割联邦学习租户的最优激励策略。通过涵盖四种代表性分割联邦学习租户类型（ViT、BERT、Whisper和LLaMA）及多种数据模态（文本、图像和音频）的大规模仿真实验表明，与现有先进方法相比，该机制可将基础模型微调速度提升最高达3.07倍，同时持续满足微调性能目标。