mlsanityData to 7 Oct 2026

JointDNN: An Efficient Training and Inference Engine for Intelligent Mobile Cloud Computing Services

arXiv 1801.08618 ↗First posted 25 Jan 2018cs.DC cs.AI cs.LGcited by 79

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Abstract (arXiv)

Deep learning models are being deployed in many mobile intelligent applications. End-side services, such as intelligent personal assistants, autonomous cars, and smart home services often employ either simple local models on the mobile or complex remote models on the cloud. However, recent studies have shown that partitioning the DNN computations between the mobile and cloud can increase the latency and energy efficiencies. In this paper, we propose an efficient, adaptive, and practical engine, JointDNN, for collaborative computation between a mobile device and cloud for DNNs in both inference and training phase. JointDNN not only provides an energy and performance efficient method of querying DNNs for the mobile side but also benefits the cloud server by reducing the amount of its workload and communications compared to the cloud-only approach. Given the DNN architecture, we investigate the efficiency of processing some layers on the mobile device and some layers on the cloud server. We provide optimization formulations at layer granularity for forward- and backward-propagations in DNNs, which can adapt to mobile battery limitations and cloud server load constraints and quality of service. JointDNN achieves up to 18 and 32 times reductions on the latency and mobile energy consumption of querying DNNs compared to the status-quo approaches, respectively.

Key facts. Cited by 79 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jan 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 20; 2022: 18; 2023: 13; 2024: 14; 2025: 6; 2026: 8.

79 papers.

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  1. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
  2. 2021Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challengescited by 114
  3. 2021AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learningcited by 39
  4. 2021Device-Cloud Collaborative Learning for Recommendationcited by 34
  5. 2021Supervised Compression for Resource-Constrained Edge Computing Systemscited by 31
  6. 2022BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computingcited by 28
  7. 2023Privacy-preserving Adversarial Facial Featurescited by 24
  8. 2022A Survey on Collaborative DNN Inference for Edge Intelligencecited by 23
  9. 2021Collaborative Intelligence: Challenges and Opportunitiescited by 18
  10. 2021Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligencecited by 18
  11. 2023Real-time Neural Network Inference on Extremely Weak Devices: Agile Offloading with Explainable AIcited by 17
  12. 2022SC2 Benchmark: Supervised Compression for Split Computingcited by 14
38 more
  1. 2021Pipeline Parallelism for Inference on Heterogeneous Edge Computingcited by 13
  2. 2022SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systemscited by 13
  3. 2022Decentralized Low-Latency Collaborative Inference via Ensembles on the Edgecited by 9
  4. 2021Autodidactic Neurosurgeon: Collaborative Deep Inference for Mobile Edge Intelligence via Online Learningcited by 8
  5. 2023Enabling Resource-efficient AIoT System with Cross-level Optimization: A surveycited by 8
  6. 2021Lightweight Compression of Intermediate Neural Network Features for Collaborative Intelligencecited by 7
  7. 2022I-SPLIT: Deep Network Interpretability for Split Computingcited by 7
  8. 2021Single-Training Collaborative Object Detectors Adaptive to Bandwidth and Computationcited by 6
  9. 2022On-Device Learning with Cloud-Coordinated Data Augmentation for Extreme Model Personalization in Recommender Systemscited by 6
  10. 2022Privacy-Preserving Visual Localization with Event Camerascited by 6
  11. 2021CALTeC: Content-Adaptive Linear Tensor Completion for Collaborative Intelligencecited by 5
  12. 2022Online Learning for Orchestration of Inference in Multi-User End-Edge-Cloud Networkscited by 5
  13. 2022T-RECX: Tiny-Resource Efficient Convolutional neural networks with early-eXitcited by 5
  14. 2023Hierarchical Training of Deep Neural Networks Using Early Exitingcited by 5
  15. 2024Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challengescited by 5
  16. 2021Fingerprinting Multi-exit Deep Neural Network Models via Inference Timecited by 4
  17. 2024Distributed Semantic Segmentation with Efficient Joint Source and Task Decodingcited by 4
  18. 2022Edge-centric Optimization of Multi-modal ML-driven eHealth Applicationscited by 3
  19. 2023A Survey on Deep Neural Network Partition over Cloud, Edge and End Devicescited by 3
  20. 2023Training Latency Minimization for Model-Splitting Allowed Federated Edge Learningcited by 3
  21. 2023Progressive Neural Compression for Adaptive Image Offloading under Timing Constraintscited by 3
  22. 2024On the Impact of Black-box Deployment Strategies for Edge AI on Latency and Model Performancecited by 3
  23. 2024Early-Exit meets Model-Distributed Inference at Edge Networkscited by 3
  24. 2024Learning the Optimal Path and DNN Partition for Collaborative Edge Inferencecited by 3
  25. 2025Energy-Aware Deep Learning on Resource-Constrained Hardwarecited by 3
  26. 2022PARIS and ELSA: An Elastic Scheduling Algorithm for Reconfigurable Multi-GPU Inference Serverscited by 2
  27. 2022Adaptive Edge Offloading for Image Classification Under Rate Limitcited by 2
  28. 2022Privacy-Preserving Feature Coding for Machinescited by 2
  29. 2023DISCO: Distributed Inference with Sparse Communicationscited by 2
  30. 2024LAECIPS: Large Vision Model Assisted Adaptive Edge-Cloud Collaboration for IoT-based Embodied Intelligence Systemcited by 2
  31. 2024Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systemscited by 2
  32. 2024MTL-Split: Multi-Task Learning for Edge Devices using Split Computingcited by 2
  33. 2024Resource-Efficient Sensor Fusion via System-Wide Dynamic Gated Neural Networkscited by 2
  34. 2024Optimizing Edge Offloading Decisions for Object Detectioncited by 2
  35. 2024Priority-Aware Model-Distributed Inference at Edge Networkscited by 2
  36. 2025Privacy-Aware Joint DNN Model Deployment and Partitioning Optimization for Collaborative Edge Inference Servicescited by 2
  37. 2021DynaComm: Accelerating Distributed CNN Training between Edges and Clouds through Dynamic Communication Schedulingcited by 1
  38. 2022License Plate Privacy in Collaborative Visual Analysis of Traffic Scenescited by 1

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