mlsanityData to 7 Oct 2026

AppealNet: An Efficient and Highly-Accurate Edge/Cloud Collaborative Architecture for DNN Inference

arXiv 2105.04104 ↗First posted 10 May 2021cs.LGcited by 16

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Abstract (arXiv)

This paper presents AppealNet, a novel edge/cloud collaborative architecture that runs deep learning (DL) tasks more efficiently than state-of-the-art solutions. For a given input, AppealNet accurately predicts on-the-fly whether it can be successfully processed by the DL model deployed on the resource-constrained edge device, and if not, appeals to the more powerful DL model deployed at the cloud. This is achieved by employing a two-head neural network architecture that explicitly takes inference difficulty into consideration and optimizes the tradeoff between accuracy and computation/communication cost of the edge/cloud collaborative architecture. Experimental results on several image classification datasets show up to more than 40% energy savings compared to existing techniques without sacrificing accuracy.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 21 of its 28 references that are in the map. First posted on arXiv 10 May 2021.

Builds on

21 of 28 references.

References · 21

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9 more
  1. 2017Adaptive Neural Networks for Efficient Inferencecited by 161
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 2; 2025: 5; 2026: 4.

16 papers.

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  1. 2023EdgeFM: Leveraging Foundation Model for Open-set Learning on the Edgecited by 21
  2. 2023DVFO: Learning-Based DVFS for Energy-Efficient Edge-Cloud Collaborative Inferencecited by 13
  3. 2025Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Surveycited by 13
  4. 2023Shoggoth: Towards Efficient Edge-Cloud Collaborative Real-Time Video Inference via Adaptive Online Learningcited by 11
  5. 2023A Survey on Deep Neural Network Partition over Cloud, Edge and End Devicescited by 3
  6. 2023MultiTASC: A Multi-Tenancy-Aware Scheduler for Cascaded DNN Inference at the Consumer Edgecited by 3
  7. 2024LAECIPS: Large Vision Model Assisted Adaptive Edge-Cloud Collaboration for IoT-based Embodied Intelligence Systemcited by 2
  8. 2024Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systemscited by 2
  9. 2025E4: Energy-Efficient DNN Inference for Edge Video Analytics Via Early-Exit and DVFScited by 2
  10. 2025High-Quality Pseudo-Label Generation Based on Visual Prompt Assisted Cloud Model Updatecited by 1
  11. 2026Cloud Is Closer Than It Appears: Revisiting the Tradeoffs of Distributed Real-Time Inferencecited by 1
  12. 2025Energy-Efficient Stochastic Computing (SC) Neural Networks for Internet of Things Devices With Layer-Wise Adjustable Sequence Length (ASL)cited by 0
4 more
  1. 2025Synera: Synergistic LLM Serving across Device and Cloud at Scalecited by 0
  2. 2026DAT: Dual-Aware Adaptive Transmission for Efficient Multimodal LLM Inference in Edge-Cloud Systemscited by 0
  3. 2026R2E-VID: Two-Stage Robust Routing via Temporal Gating for Elastic Edge-Cloud Video Inferencecited by 0
  4. 2026E2LLM: Towards Efficient LLM Serving in Heterogeneous Edge/Fog Environmentscited by 0