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Fault-Tolerant Collaborative Inference through the Edge-PRUNE Framework

arXiv 2206.08152 ↗First posted 16 Jun 2022cs.LG cs.DCcited by 0

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Abstract (arXiv)

Collaborative inference has received significant research interest in machine learning as a vehicle for distributing computation load, reducing latency, as well as addressing privacy preservation in communications. Recent collaborative inference frameworks have adopted dynamic inference methodologies such as early-exit and run-time partitioning of neural networks. However, as machine learning frameworks scale in the number of inference inputs, e.g., in surveillance applications, fault tolerance related to device failure needs to be considered. This paper presents the Edge-PRUNE distributed computing framework, built on a formally defined model of computation, which provides a flexible infrastructure for fault tolerant collaborative inference. The experimental section of this work shows results on achievable inference time savings by collaborative inference, presents fault tolerant system topologies and analyzes their cost in terms of execution time overhead.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 33 references that are in the map. First posted on arXiv 16 Jun 2022.

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References · 18

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6 more
  1. 2021Supervised Compression for Resource-Constrained Edge Computing Systemscited by 31
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  3. 2021DynO: Dynamic Onloading of Deep Neural Networks from Cloud to Devicecited by 17
  4. 2022SplitNets: Designing Neural Architectures for Efficient Distributed Computing on Head-Mounted Systemscited by 13
  5. 2021Dynamic DNN Decomposition for Lossless Synergistic Inferencecited by 3
  6. 2022Edge-PRUNE: Flexible Distributed Deep Learning Inferencecited by 1

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  2. 2022Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing
  3. 2025DistrEE: Distributed Early Exit of Deep Neural Network Inference on Edge Devices
  4. 2024Collaborative Inference for Large Models with Task Offloading and Early Exiting
  5. 2022Receptive Field-based Segmentation for Distributed CNN Inference Acceleration in Collaborative Edge Computing
  6. 2023Adaptive Early Exiting for Collaborative Inference over Noisy Wireless Channels
  7. 2025Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges
  8. 2024Federated Learning for Collaborative Inference Systems: The Case of Early Exit Networks