# Packet-Loss-Tolerant Split Inference for Delay-Sensitive Deep Learning in Lossy Wireless Networks

arXiv: https://arxiv.org/abs/2104.13629 · HTML: https://mlsanity.com/paper/2104.13629/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 13 references that are in the map.
- First posted on arXiv 28 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Understanding How Image Quality Affects Deep Neural Networks](https://mlsanity.com/paper/1604.04004/) (2016)
- [Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud](https://mlsanity.com/paper/1809.03428/) (2018)
- [Communication-Efficient Multimodal Split Learning for mmWave Received Power Prediction](https://mlsanity.com/paper/2003.00645/) (2020)
- [Memory- and Communication-Aware Model Compression for Distributed Deep Learning Inference on IoT](https://mlsanity.com/paper/1907.11804/) (2019)

## Built on by (most cited first, 7 of 7)

- [Communication-oriented Model Fine-tuning for Packet-loss Resilient Distributed Inference under Highly Lossy IoT Networks](https://mlsanity.com/paper/2112.09407/) (2021)
- [SALT: A Lightweight Model Adaptation Method for Closed Split Computing Environments](https://mlsanity.com/paper/2506.07355/) (2025)
- [Bayes-Split-Edge: Bayesian Optimization for Constrained Collaborative Inference in Wireless Edge Systems](https://mlsanity.com/paper/2510.23503/) (2025)
- [Watch from sky: machine-learning-based multi-UAV network for predictive police surveillance](https://mlsanity.com/paper/2203.02892/) (2022)
- [Efficient IoT Inference via Context-Awareness](https://mlsanity.com/paper/2310.19112/) (2023)
- [Aligned Vector Quantization for Edge-Cloud Collabrative Vision-Language Models](https://mlsanity.com/paper/2411.05961/) (2024)
- [Lightweight User-Personalization Method for Closed Split Computing](https://mlsanity.com/paper/2603.14958/) (2026)

## Abstract (arXiv)

The distributed inference framework is an emerging technology for real-time applications empowered by cutting-edge deep machine learning (ML) on resource-constrained Internet of things (IoT) devices. In distributed inference, computational tasks are offloaded from the IoT device to other devices or the edge server via lossy IoT networks. However, narrow-band and lossy IoT networks cause non-negligible packet losses and retransmissions, resulting in non-negligible communication latency. This study solves the problem of the incremental retransmission latency caused by packet loss in a lossy IoT network. We propose a split inference with no retransmissions (SI-NR) method that achieves high accuracy without any retransmissions, even when packet loss occurs. In SI-NR, the key idea is to train the ML model by emulating the packet loss by a dropout method, which randomly drops the output of hidden units in a DNN layer. This enables the SI-NR system to obtain robustness against packet losses. Our ML experimental evaluation reveals that SI-NR obtains accurate predictions without packet retransmission at a packet loss rate of 60%.