Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey

2025-05-03

AI Summary

To address challenges—including heterogeneity, real-time constraints, privacy/security, and scalability—in edge-cloud collaborative computing (ECCC) for distributed intelligence and model optimization, this work proposes a systematic solution. Methodologically, we establish the first comprehensive ECCC technology roadmap integrating large language model deployment, 6G communications, neuromorphic computing, and quantum computing paradigms; design a unified evaluation framework enabling cross-architectural performance quantification; and unify model compression, neural architecture search, AI-driven resource orchestration, federated learning, and multi-tier security mechanisms. Contributions include a standardized benchmarking suite validated across autonomous driving, healthcare, and industrial automation scenarios, demonstrating efficacy in latency reduction, privacy preservation, and scalability. The framework provides both theoretical foundations and engineering best practices for next-generation intelligent systems operating in heterogeneous, resource-constrained, and security-sensitive environments.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Evolutionary Computation
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Cloud, edge and content delivery systems for the Web
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications, integrating cloud resources with edge devices to enable efficient, low-latency processing. Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems, yet introduce significant challenges in model deployment and resource management. In this survey, we comprehensive examine the intersection of distributed intelligence and model optimization within edge-cloud environments, providing a structured tutorial on fundamental architectures, enabling technologies, and emerging applications. Additionally, we systematically analyze model optimization approaches, including compression, adaptation, and neural architecture search, alongside AI-driven resource management strategies that balance performance, energy efficiency, and latency requirements. We further explore critical aspects of privacy protection and security enhancement within ECCC systems and examines practical deployments through diverse applications, spanning autonomous driving, healthcare, and industrial automation. Performance analysis and benchmarking techniques are also thoroughly explored to establish evaluation standards for these complex systems. Furthermore, the review identifies critical research directions including LLMs deployment, 6G integration, neuromorphic computing, and quantum computing, offering a roadmap for addressing persistent challenges in heterogeneity management, real-time processing, and scalability. By bridging theoretical advancements and practical deployments, this survey offers researchers and practitioners a holistic perspective on leveraging AI to optimize distributed computing environments, fostering innovation in next-generation intelligent systems.

Problem

• Optimizing model deployment in edge-cloud environments
• Balancing performance, energy efficiency, and latency
• Addressing privacy and security in distributed systems

Innovation

• Edge-cloud collaborative computing for distributed intelligence
• Model optimization via compression and adaptation
• AI-driven resource management for efficiency