An Edge-Cloud Collaboration Framework for Generative AI Service Provision with Synergetic Big Cloud Model and Small Edge Models
(2401.01666)Abstract
Generative artificial intelligence (GenAI) offers various services to users through content creation, which is believed to be one of the most important components in future networks. However, training and deploying big artificial intelligence models (BAIMs) introduces substantial computational and communication overhead.This poses a critical challenge to centralized approaches, due to the need of high-performance computing infrastructure and the reliability, secrecy and timeliness issues in long-distance access of cloud services. Therefore, there is an urging need to decentralize the services, partly moving them from the cloud to the edge and establishing native GenAI services to enable private, timely, and personalized experiences. In this paper, we propose a brand-new bottom-up BAIM architecture with synergetic big cloud model and small edge models, and design a distributed training framework and a task-oriented deployment scheme for efficient provision of native GenAI services. The proposed framework can facilitate collaborative intelligence, enhance adaptability, gather edge knowledge and alleviate edge-cloud burden. The effectiveness of the proposed framework is demonstrated through an image generation use case. Finally, we outline fundamental research directions to fully exploit the collaborative potential of edge and cloud for native GenAI and BAIM applications.
Overview
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The paper presents an edge-cloud collaboration framework for distributing Generative AI services using small edge models and a larger cloud model.
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It addresses challenges such as adaptability, local knowledge acquisition at the edge, and reducing computational burdens on central cloud servers.
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A two-tiered gating network, HierGate, is detailed for selecting optimal edge models to work with the cloud model, along with continuous model training strategies.
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Empirical validation using image generation with variational autoencoders and Frechet Inception Distance metrics is discussed, showing improved image quality with the framework.
Overview of the Framework
Recent advancements in Generative AI (GenAI) services, particularly those that produce content like images, text, and videos, have called for more efficient deployment solutions. In light of the substantial compute and communication demands of big AI models (BAIMs), the paper underscores the urgency in decentralizing these services to the edge of the network. By integrating small edge models with a larger cloud model, the authors propose a novel edge-cloud collaboration framework designed for native GenAI service provision. This approach aims to reduce the computational load on centralized infrastructures, improve data security, ensure timely responses, and offer personalized services.
The Challenges Addressed
The proposed framework tackles three major challenges: adaptability, edge knowledge acquisition, and mitigation of cloud burden. The model adapts to varying communication, computation, and storage capacities across network nodes and has the ability to learn from local edge data, allowing for the generation of more sophisticated BAIMs. Additionally, a distributed approach to training and deploying models helps lower demands on data storage, processing, and communication on central servers, which aligns with the ecological and economic goals.
Architecture and Model Training Considerations
Focused on the bottom-up BAIM architecture, the paper outlines a two-tiered gating network concept (HierGate) that selects top-performing edge models for each user task. In this setup, the cloud server conducts fine-tuning or freezing strategies on the BAIM, bolstered by updating procedures such as continual learning, pruning, and few-shot learning to harness emerging knowledge from different nodes. This ensures the adaptability of the system to evolving tasks without degrading its core performance.
Demonstrating the Framework with Image Generation
An empirical validation through an image generation case study is presented, employing variational autoencoders (VAEs) across multiple edge nodes. Engagingly, fine-tuning of the central model on the cloud, followed by edge personalization, stands out in improving the quality of generated images. Quantitative measurements, such as the Frechet Inception Distance (FID), confirm the finetuning strategy's superior effectiveness over alternative training approaches.
Concluding Prospects
Although the introduced framework marks a promising step towards distributed BAIM architecture for native GenAI provisioning, it surfaces several research challenges. Future directions include managing user data securely, creating more robust model fusion schemes, adapting to dynamic edge network changes, and devising steadfast mechanisms against security threats. The paper's insights magnify the prospect that these improvements, both in technology and operational modality, can substantiate the full potential of edge-cloud collaboration in GenAI services.
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