Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey
arXiv:2505.01821 · doi:10.1109/COMST.2026.3669216
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.
Accepted by IEEE ComST. 45 pages, 13 figures, 10 tables
References in corpus (38)
- Convergence of Edge Computing and Deep Learning: A Comprehensive Survey
- Remote patient monitoring using artificial intelligence: Current state, applications, and challenges
- Edge YOLO: Real-Time Intelligent Object Detection System Based on Edge-Cloud Cooperation in Autonomous Vehicles
- A Survey on Edge Computing Systems and Tools
- Electrical Load Forecasting Using Edge Computing and Federated Learning
- Dynamic Scheduling for Stochastic Edge-Cloud Computing Environments using A3C learning and Residual Recurrent Neural Networks
- More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence
- A Review on Computational Intelligence Techniques in Cloud and Edge Computing
- Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review
- Automated machine learning: AI-driven decision making in business analytics
- Secure V2V and V2I Communication in Intelligent Transportation using Cloudlets
- Edge Federation: Towards an Integrated Service Provisioning Model
- Emerging Edge Computing Technologies for Distributed Internet of Things (IoT) Systems
- FedStack: Personalized activity monitoring using stacked federated learning
- WebAssembly as a Common Layer for the Cloud-edge Continuum
- Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey
- Towards Communication-efficient and Attack-Resistant Federated Edge Learning for Industrial Internet of Things
- Using Cloud and Fog Computing for Large Scale IoT-based Urban Sound Classification
- A Survey of Deep Learning for Data Caching in Edge Network
- Performance and Energy-Aware Bi-objective Tasks Scheduling for Cloud Data Centers
- Sustainable Edge Computing: Challenges and Future Directions
- Pareto-Optimal Bit Allocation for Collaborative Intelligence
- The Synergy of Complex Event Processing and Tiny Machine Learning in Industrial IoT
- Equilibrium in the Computing Continuum through Active Inference
- EdgeMatrix: A Resource-Redefined Scheduling Framework for SLA-Guaranteed Multi-Tier Edge-Cloud Computing Systems
- Blockchain-based Transparency Framework for Privacy Preserving Third-party Services
- Zero-touch realization of Pervasive Artificial Intelligence-as-a-service in 6G networks
- Towards characterization of edge-cloud continuum
- Using Metrics Suites to Improve the Measurement of Privacy in Graphs
- Engineering and Experimentally Benchmarking a Container-based Edge Computing System
- Resilience Enhancement at Edge Cloud Systems
- A Management Framework for Secure Multiparty Computation in Dynamic Environments
- iQuantum: A Case for Modeling and Simulation of Quantum Computing Environments
- Deep-Edge: An Efficient Framework for Deep Learning Model Update on Heterogeneous Edge
- KheOps: Cost-effective Repeatability, Reproducibility, and Replicability of Edge-to-Cloud Experiments
- ACE: Towards Application-Centric Edge-Cloud Collaborative Intelligence
- Latency-Memory Optimized Splitting of Convolution Neural Networks for Resource Constrained Edge Devices
- Enhancing Block-Wise Transfer with Network Coding in CoAP