Federated Intelligence: When Large AI Models Meet Federated Fine-Tuning and Collaborative Reasoning at the Network Edge

TL;DR

The paper tackles the challenge of deploying large AI models in resource-constrained wireless networks by proposing Federated Intelligence, which combines three federated fine-tuning schemes—Clustered Federated Fine-Tuning, Hierarchical Federated Fine-Tuning, and Asynchronous Federated Fine-Tuning—and three collaborative reasoning paradigms—Decentralized Horizontal, Cloud-Edge-End Vertical, and Multi-Access. It provides convergence analysis for federated fine-tuning and demonstrates, through simulations on tasks involving LLaMA-7B/OpenOrca and GPT2-Small, that the proposed methods reduce fine-tuning loss and achieve faster convergence while preserving user privacy. The work also discusses resource allocation, interoperability, multi-modal collaboration, opt-out privacy, and token-based communications as key open challenges, offering a roadmap for scalable, privacy-preserving edge intelligence. Overall, the approach enables distributed adaptation and reasoning for large AI models at the network edge, potentially reducing communication overhead and latency while enhancing personalization and reliability in real-world wireless networks.

Abstract

Large artificial intelligence (AI) models exhibit remarkable capabilities in various application scenarios, but deploying them at the network edge poses significant challenges due to issues such as data privacy, computational resources, and latency. In this paper, we explore federated fine-tuning and collaborative reasoning techniques to facilitate the implementation of large AI models in resource-constrained wireless networks. Firstly, promising applications of large AI models within specific domains are discussed. Subsequently, federated fine-tuning methods are proposed to adapt large AI models to specific tasks or environments at the network edge, effectively addressing the challenges associated with communication overhead and enhancing communication efficiency. These methodologies follow clustered, hierarchical, and asynchronous paradigms to effectively tackle privacy issues and eliminate data silos. Furthermore, to enhance operational efficiency and reduce latency, efficient frameworks for model collaborative reasoning are developed, which include decentralized horizontal collaboration, cloud-edge-end vertical collaboration, and multi-access collaboration. Next, simulation results demonstrate the effectiveness of our proposed methods in reducing the fine-tuning loss of large AI models across various downstream tasks. Finally, several open challenges and research opportunities are outlined.

Figures (6)

• Figure 1: Diverse application scenarios of large AI models, and different federated learning methods for fine-tuning models in wireless networks.
• Figure 2: An illustration of the decentralized schemes for horizontal collaborative reasoning of large AI models at the network edge.
• Figure 3: An illustration of cloud-edge-end vertical collaborative reasoning.
• Figure 4: The multi-access collaborative reasoning framework.
• Figure 5: Performance of fine-tuning LLaMA-7B on OpenOrca dataset.