mlsanityData to 7 Oct 2026

BottleNet: A Deep Learning Architecture for Intelligent Mobile Cloud Computing Services

arXiv 1902.01000 ↗First posted 4 Feb 2019cs.DC cs.LGcited by 67

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Abstract (arXiv)

Recent studies have shown the latency and energy consumption of deep neural networks can be significantly improved by splitting the network between the mobile device and cloud. This paper introduces a new deep learning architecture, called BottleNet, for reducing the feature size needed to be sent to the cloud. Furthermore, we propose a training method for compensating for the potential accuracy loss due to the lossy compression of features before transmitting them to the cloud. BottleNet achieves on average 30x improvement in end-to-end latency and 40x improvement in mobile energy consumption compared to the cloud-only approach with negligible accuracy loss.

Key facts. Cited by 67 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Feb 2019.

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Citing papers per year: 2021: 15; 2022: 18; 2023: 11; 2024: 12; 2025: 5; 2026: 6.

67 papers.

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  1. 2022Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communicationscited by 166
  2. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 125
  3. 2021Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challengescited by 114
  4. 2021Task-Oriented Communication for Multi-Device Cooperative Edge Inferencecited by 47
  5. 2022ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learningcited by 36
  6. 2021Supervised Compression for Resource-Constrained Edge Computing Systemscited by 31
  7. 2022BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computingcited by 28
  8. 2023Green Edge AI: A Contemporary Surveycited by 24
  9. 2023Privacy and Efficiency of Communications in Federated Split Learningcited by 20
  10. 2021Lightweight compression of neural network feature tensors for collaborative intelligencecited by 17
  11. 2022SC2 Benchmark: Supervised Compression for Split Computingcited by 14
  12. 2022SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systemscited by 13
38 more
  1. 2022Task-Oriented Communication for Edge Video Analyticscited by 13
  2. 2023SplitEE: Early Exit in Deep Neural Networks with Split Computingcited by 13
  3. 2022Multi-Agent Collaborative Inference via DNN Decoupling: Intermediate Feature Compression and Edge Learningcited by 12
  4. 2023FrankenSplit: Efficient Neural Feature Compression with Shallow Variational Bottleneck Injection for Mobile Edge Computingcited by 12
  5. 2022SplitPlace: AI Augmented Splitting and Placement of Large-Scale Neural Networks in Mobile Edge Environmentscited by 11
  6. 2024SplitLLM: Collaborative Inference of LLMs for Model Placement and Throughput Optimizationcited by 10
  7. 2024I-SplitEE: Image classification in Split Computing DNNs with Early Exitscited by 9
  8. 2021Autodidactic Neurosurgeon: Collaborative Deep Inference for Mobile Edge Intelligence via Online Learningcited by 8
  9. 2021Privacy Vulnerability of Split Computing to Data-Free Model Inversion Attackscited by 8
  10. 2022C3-SL: Circular Convolution-Based Batch-Wise Compression for Communication-Efficient Split Learningcited by 8
  11. 2023Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiencycited by 8
  12. 2021Lightweight Compression of Intermediate Neural Network Features for Collaborative Intelligencecited by 7
  13. 2022Feature Compression for Rate Constrained Object Detection on the Edgecited by 7
  14. 2022I-SPLIT: Deep Network Interpretability for Split Computingcited by 7
  15. 2022Over-the-Air Split Machine Learning in Wireless MIMO Networkscited by 7
  16. 2021Single-Training Collaborative Object Detectors Adaptive to Bandwidth and Computationcited by 6
  17. 2025A Multi-task Supervised Compression Model for Split Computingcited by 6
  18. 2025SL-ACC: A Communication-Efficient Split Learning Framework with Adaptive Channel-wise Compressioncited by 6
  19. 2026Embodied Foundation Models at the Edge: A Survey of Deployment Constraints and Mitigation Strategiescited by 6
  20. 2021CALTeC: Content-Adaptive Linear Tensor Completion for Collaborative Intelligencecited by 5
  21. 2022A Low-Complexity Approach to Rate-Distortion Optimized Variable Bit-Rate Compression for Split DNN Computingcited by 5
  22. 2024Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challengescited by 5
  23. 2025Diff-ICMH: Harmonizing Machine and Human Vision in Image Compression with Generative Priorcited by 4
  24. 2022Romanus: Robust Task Offloading in Modular Multi-Sensor Autonomous Driving Systemscited by 3
  25. 2022Neural Architecture Search for Improving Latency-Accuracy Trade-off in Split Computingcited by 3
  26. 2023Slimmable Encoders for Flexible Split DNNs in Bandwidth and Resource Constrained IoT Systemscited by 3
  27. 2023Progressive Neural Compression for Adaptive Image Offloading under Timing Constraintscited by 3
  28. 2023Salted Inference: Enhancing Privacy while Maintaining Efficiency of Split Inference in Mobile Computingcited by 3
  29. 2023Adaptive Early Exiting for Collaborative Inference over Noisy Wireless Channelscited by 3
  30. 2024On the Impact of Black-box Deployment Strategies for Edge AI on Latency and Model Performancecited by 3
  31. 2024Learning the Optimal Path and DNN Partition for Collaborative Edge Inferencecited by 3
  32. 2024Distributed Inference on Mobile Edge and Cloud: An Early Exit based Clustering Approachcited by 3
  33. 2024SpikeBottleNet: Spike-Driven Feature Compression Architecture for Edge-Cloud Co-Inferencecited by 3
  34. 2022Adaptive Edge Offloading for Image Classification Under Rate Limitcited by 2
  35. 2022Privacy-Preserving Feature Coding for Machinescited by 2
  36. 2024Optimizing Edge Offloading Decisions for Object Detectioncited by 2
  37. 2026Compression Tells Intelligence: Visual Coding, Visual Token Technology, and the Unificationcited by 2
  38. 2023Adversarial Attacks to Latent Representations of Distributed Neural Networks in Split Computingcited by 1

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