mlsanityData to 7 Oct 2026

Edge-Host Partitioning of Deep Neural Networks with Feature Space Encoding for Resource-Constrained Internet-of-Things Platforms

arXiv 1802.03835 ↗First posted 11 Feb 2018cs.CVcited by 35

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Abstract (arXiv)

This paper introduces partitioning an inference task of a deep neural network between an edge and a host platform in the IoT environment. We present a DNN as an encoding pipeline, and propose to transmit the output feature space of an intermediate layer to the host. The lossless or lossy encoding of the feature space is proposed to enhance the maximum input rate supported by the edge platform and/or reduce the energy of the edge platform. Simulation results show that partitioning a DNN at the end of convolutional (feature extraction) layers coupled with feature space encoding enables significant improvement in the energy-efficiency and throughput over the baseline configurations that perform the entire inference at the edge or at the host.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Feb 2018.

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Citing papers per year: 2021: 11; 2022: 5; 2023: 13; 2024: 4; 2025: 2.

35 papers.

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  1. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
  2. 2021Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AIcited by 38
  3. 2023Privacy-preserving Adversarial Facial Featurescited by 24
  4. 2021LegoDNN: Block-grained Scaling of Deep Neural Networks for Mobile Visioncited by 20
  5. 2021Optimal Model Placement and Online Model Splitting for Device-Edge Co-Inferencecited by 19
  6. 2021Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligencecited by 18
  7. 2023Real-time Neural Network Inference on Extremely Weak Devices: Agile Offloading with Explainable AIcited by 17
  8. 2023DistHD: A Learner-Aware Dynamic Encoding Method for Hyperdimensional Classificationcited by 13
  9. 2023ProgDTD: Progressive Learned Image Compression with Double-Tail-Drop Trainingcited by 13
  10. 2022Multi-user Co-inference with Batch Processing Capable Edge Servercited by 11
  11. 2021On-edge Multi-task Transfer Learning: Model and Practice with Data-driven Task Allocationcited by 9
  12. 2023PATROL: Privacy-Oriented Pruning for Collaborative Inference Against Model Inversion Attackscited by 9
23 more
  1. 2022C3-SL: Circular Convolution-Based Batch-Wise Compression for Communication-Efficient Split Learningcited by 8
  2. 2023Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiencycited by 8
  3. 2021Edge-Cloud Collaborated Object Detection via Difficult-Case Discriminatorcited by 7
  4. 2021SensiX++: Bringing MLOPs and Multi-tenant Model Serving to Sensory Edge Devicescited by 7
  5. 2022Edge Video Analytics: A Survey on Applications, Systems and Enabling Techniquescited by 7
  6. 2024Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challengescited by 5
  7. 2021DeepScale: Online Frame Size Adaptation for Multi-object Tracking on Smart Cameras and Edge Serverscited by 4
  8. 2023A Survey on Deep Neural Network Partition over Cloud, Edge and End Devicescited by 3
  9. 2023Mobility and Cost Aware Inference Accelerating Algorithm for Edge Intelligencecited by 3
  10. 2022Layer-Wise Partitioning and Merging for Efficient and Scalable Deep Learningcited by 2
  11. 2023Robust and Scalable Hyperdimensional Computing With Brain-Like Neural Adaptationscited by 2
  12. 2025Communication Efficient Split Learning of ViTs with Attention-based Double Compressioncited by 2
  13. 2023Edge AI for Internet of Energy: Challenges and Perspectivescited by 1
  14. 2024opp/ai: Optimistic Privacy-Preserving AI on Blockchaincited by 1
  15. 2021ScissionLite: Accelerating Distributed Deep Neural Networks Using Transfer Layercited by 0
  16. 2021FTPipeHD: A Fault-Tolerant Pipeline-Parallel Distributed Training Framework for Heterogeneous Edge Devicescited by 0
  17. 2022An Adaptive Device-Edge Co-Inference Framework Based on Soft Actor-Criticcited by 0
  18. 2023Facial Data Minimization: Shallow Model as Your Privacy Filtercited by 0
  19. 2023AccEPT: An Acceleration Scheme for Speeding Up Edge Pipeline-parallel Trainingcited by 0
  20. 2023High Efficiency Inference Accelerating Algorithm for NOMA-based Mobile Edge Computingcited by 0
  21. 2024Emergency Computing: An Adaptive Collaborative Inference Method Based on Hierarchical Reinforcement Learningcited by 0
  22. 2024Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed Banditcited by 0
  23. 2025Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learningcited by 0