# Improving Device-Edge Cooperative Inference of Deep Learning via 2-Step Pruning

arXiv: https://arxiv.org/abs/1903.03472 · HTML: https://mlsanity.com/paper/1903.03472/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2019.

## Built on by (most cited first, 25 of 25)

- [Distributed Learning in Wireless Networks: Recent Progress and Future Challenges](https://mlsanity.com/paper/2104.02151/) (2021)
- [Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach](https://mlsanity.com/paper/2102.04170/) (2021)
- [Task-Oriented Sensing, Computation, and Communication Integration for Multi-Device Edge AI](https://mlsanity.com/paper/2207.00969/) (2022)
- [Green Edge AI: A Contemporary Survey](https://mlsanity.com/paper/2312.00333/) (2023)
- [Optimal Model Placement and Online Model Splitting for Device-Edge Co-Inference](https://mlsanity.com/paper/2105.13618/) (2021)
- [Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence](https://mlsanity.com/paper/2105.01798/) (2021)
- [Digital-SC: Digital Semantic Communication with Adaptive Network Split and Learned Non-Linear Quantization](https://mlsanity.com/paper/2305.13553/) (2023)
- [Multi-Agent Collaborative Inference via DNN Decoupling: Intermediate Feature Compression and Edge Learning](https://mlsanity.com/paper/2205.11854/) (2022)
- [Multi-user Co-inference with Batch Processing Capable Edge Server](https://mlsanity.com/paper/2206.06304/) (2022)
- [Adaptive Compression-Aware Split Learning and Inference for Enhanced Network Efficiency](https://mlsanity.com/paper/2311.05739/) (2023)
- [Edge-Cloud Collaborated Object Detection via Difficult-Case Discriminator](https://mlsanity.com/paper/2108.12858/) (2021)
- [Communication-oriented Model Fine-tuning for Packet-loss Resilient Distributed Inference under Highly Lossy IoT Networks](https://mlsanity.com/paper/2112.09407/) (2021)
- [Accelerating End-Cloud Collaborative Inference via Near Bubble-free Pipeline Optimization](https://mlsanity.com/paper/2501.12388/) (2024)
- [The Larger the Merrier? Efficient Large AI Model Inference in Wireless Edge Networks](https://mlsanity.com/paper/2505.09214/) (2025)
- [Efficient Multiuser AI Downloading via Reusable Knowledge Broadcasting](https://mlsanity.com/paper/2307.15316/) (2023)
- [Collaborative Edge AI Inference over Cloud-RAN](https://mlsanity.com/paper/2404.06007/) (2024)
- [Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels](https://mlsanity.com/paper/2106.00995/) (2021)
- [Communication-Computation Efficient Device-Edge Co-Inference via AutoML](https://mlsanity.com/paper/2108.13009/) (2021)
- [Priority-Aware Model-Distributed Inference at Edge Networks](https://mlsanity.com/paper/2412.12371/) (2024)
- [Aligning Task- and Reconstruction-Oriented Communications for Edge Intelligence](https://mlsanity.com/paper/2502.15472/) (2025)
- [Onboard Optimization and Learning: A Survey](https://mlsanity.com/paper/2505.08793/) (2025)
- [QPART: Adaptive Model Quantization and Dynamic Workload Balancing for Accuracy-aware Edge Inference](https://mlsanity.com/paper/2506.23934/) (2025)
- [Emergency Computing: An Adaptive Collaborative Inference Method Based on Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2402.02146/) (2024)
- [Task-Oriented Co-Design of Communication, Computing, and Control for Edge-Enabled Industrial Cyber-Physical Systems](https://mlsanity.com/paper/2503.08661/) (2025)
- [Hierarchical Online-Scheduling for Energy-Efficient Split Inference with Progressive Transmission](https://mlsanity.com/paper/2601.08135/) (2026)

## Abstract (arXiv)

Deep neural networks (DNNs) are state-of-the-art solutions for many machine learning applications, and have been widely used on mobile devices. Running DNNs on resource-constrained mobile devices often requires the help from edge servers via computation offloading. However, offloading through a bandwidth-limited wireless link is non-trivial due to the tight interplay between the computation resources on mobile devices and wireless resources. Existing studies have focused on cooperative inference where DNN models are partitioned at different neural network layers, and the two parts are executed at the mobile device and the edge server, respectively. Since the output data size of a DNN layer can be larger than that of the raw data, offloading intermediate data between layers can suffer from high transmission latency under limited wireless bandwidth. In this paper, we propose an efficient and flexible 2-step pruning framework for DNN partition between mobile devices and edge servers. In our framework, the DNN model only needs to be pruned once in the training phase where unimportant convolutional filters are removed iteratively. By limiting the pruning region, our framework can greatly reduce either the wireless transmission workload of the device or the total computation workload. A series of pruned models are generated in the training phase, from which the framework can automatically select to satisfy varying latency and accuracy requirements. Furthermore, coding for the intermediate data is added to provide extra transmission workload reduction. Our experiments show that the proposed framework can achieve up to 25.6× reduction on transmission workload, 6.01× acceleration on total computation and 4.81× reduction on end-to-end latency as compared to partitioning the original DNN model without pruning.