mlsanityData to 14 Oct 2026

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

arXiv 2505.01821 ↗First posted 3 May 2025cs.DC cs.AI cs.LGcited by 14

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Abstract (arXiv)

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.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 226 of its 364 references that are in the map. First posted on arXiv 3 May 2025.

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226 of 364 references.

References · 226

  1. 2023EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thoughtcited by 279
  2. 2021Trustworthy AI: From Principles to Practicescited by 207
  3. 2021Federated Learning for Smart Healthcare: A Surveycited by 184
  4. 2024Mobile Edge Intelligence for Large Language Models: A Contemporary Surveycited by 128
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  11. 2023Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Servicescited by 76
  12. 2023EdgeMoE: Empowering Sparse Large Language Models on Mobile Devicescited by 69
38 more
  1. 2023Split Learning in 6G Edge Networkscited by 68
  2. 2020CoEdge: Cooperative DNN Inference with Adaptive Workload Partitioning over Heterogeneous Edge Devicescited by 67
  3. 2020End-to-End Evaluation of Federated Learning and Split Learning for Internet of Thingscited by 64
  4. 2020Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge based Frameworkcited by 59
  5. 2021Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edgecited by 56
  6. 2020Energy-Efficient Resource Management for Federated Edge Learning with CPU-GPU Heterogeneous Computingcited by 39
  7. 2021Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AIcited by 39
  8. 2022Electrical Load Forecasting Using Edge Computing and Federated Learningcited by 34
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Citing papers per year: 2025: 3; 2026: 11.

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  1. 2025A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networkscited by 3
  2. 2026FlexSpec: Frozen Drafts Meet Evolving Targets in Edge-Cloud Collaborative LLM Speculative Decodingcited by 3
  3. 2026Diffusion-Guided Semantic Consistency for Multimodal Heterogeneitycited by 2
  4. 2026Collaborative Adaptive Curriculum for Progressive Knowledge Distillationcited by 2
  5. 2025CollaPipe: Adaptive Segment-Optimized Pipeline Parallelism for Collaborative LLM Training in Heterogeneous Edge Networkscited by 1
  6. 2025M2S2L: Mamba-based Multi-Scale Spatial-temporal Learning for Video Anomaly Detectioncited by 1
  7. 2026Node Learning: A Framework for Adaptive, Decentralised and Collaborative Network Edge AIcited by 0
  8. 2026Motion Semantics Guided Normalizing Flow for Privacy-Preserving Video Anomaly Detectioncited by 0
  9. 2026R2E-VID: Two-Stage Robust Routing via Temporal Gating for Elastic Edge-Cloud Video Inferencecited by 0
  10. 2026Multi-Agent Systems: From Classical Paradigms to Large Foundation Model-Enabled Futurescited by 0
  11. 2026Continual Learning with Elastic Regularization and Synthetic Replay for Federated MLLM Fine-Tuningcited by 0
  12. 2026Learning-Based Collaborative MEC for LLM Inference with Soft-Deadline Awareness via Transformer-Enhanced PPOcited by 0
2 more
  1. 2026Large Models for Small Devices: Recent Advances and Empirical Analysis of Edge AI Deploymentcited by 0
  2. 2026Exposing AI Inference as a Telco Network API over the 5G User Planecited by 0