# DiReDi: Distillation and Reverse Distillation for AIoT Applications

arXiv: https://arxiv.org/abs/2409.08308 · HTML: https://mlsanity.com/paper/2409.08308/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 50 references that are in the map.
- First posted on arXiv 12 Sep 2024.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [FitNets: Hints for Thin Deep Nets](https://mlsanity.com/paper/1412.6550/) (2014)
- [Knowledge Distillation: A Survey](https://mlsanity.com/paper/2006.05525/) (2020)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [An Introduction to Convolutional Neural Networks](https://mlsanity.com/paper/1511.08458/) (2015)
- [Knowledge Distillation and Student-Teacher Learning for Visual Intelligence: A Review and New Outlooks](https://mlsanity.com/paper/2004.05937/) (2020)
- [Distilling Object Detectors with Fine-grained Feature Imitation](https://mlsanity.com/paper/1906.03609/) (2019)
- [Channel-wise Knowledge Distillation for Dense Prediction](https://mlsanity.com/paper/2011.13256/) (2020)
- [Focal and Global Knowledge Distillation for Detectors](https://mlsanity.com/paper/2111.11837/) (2021)
- [General Instance Distillation for Object Detection](https://mlsanity.com/paper/2103.02340/) (2021)
- [Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things](https://mlsanity.com/paper/2011.08612/) (2020)
- [Localization Distillation for Dense Object Detection](https://mlsanity.com/paper/2102.12252/) (2021)
- [FSSD: Feature Fusion Single Shot Multibox Detector](https://mlsanity.com/paper/1712.00960/) (2017)
- [PKD: General Distillation Framework for Object Detectors via Pearson Correlation Coefficient](https://mlsanity.com/paper/2207.02039/) (2022)
- [Distilling Object Detectors with Task Adaptive Regularization](https://mlsanity.com/paper/2006.13108/) (2020)
- [Student Network Learning via Evolutionary Knowledge Distillation](https://mlsanity.com/paper/2103.13811/) (2021)
- [Distilling a Powerful Student Model via Online Knowledge Distillation](https://mlsanity.com/paper/2103.14473/) (2021)
- [Edge AI-Enabled Chicken Health Detection Based on Enhanced FCOS-Lite and Knowledge Distillation](https://mlsanity.com/paper/2407.09562/) (2024)
- [Federated Learning over a Wireless Network: Distributed User Selection through Random Access](https://mlsanity.com/paper/2307.03758/) (2023)

## Built on by (most cited first, 2 of 2)

- [ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities](https://mlsanity.com/paper/2604.01239/) (2026)
- [LSTrans: Efficient Knowledge Transfer for Lightweight and Automated ECG Classification](https://mlsanity.com/paper/2607.10784/) (2026)

## Abstract (arXiv)

Typically, the significant efficiency can be achieved by deploying different edge AI models in various real world scenarios while a few large models manage those edge AI models remotely from cloud servers. However, customizing edge AI models for each user's specific application or extending current models to new application scenarios remains a challenge. Inappropriate local training or fine tuning of edge AI models by users can lead to model malfunction, potentially resulting in legal issues for the manufacturer. To address aforementioned issues, this paper proposes an innovative framework called "DiReD", which involves knowledge DIstillation & REverse DIstillation. In the initial step, an edge AI model is trained with presumed data and a KD process using the cloud AI model in the upper management cloud server. This edge AI model is then dispatched to edge AI devices solely for inference in the user's application scenario. When the user needs to update the edge AI model to better fit the actual scenario, the reverse distillation (RD) process is employed to extract the knowledge: the difference between user preferences and the manufacturer's presumptions from the edge AI model using the user's exclusive data. Only the extracted knowledge is reported back to the upper management cloud server to update the cloud AI model, thus protecting user privacy by not using any exclusive data. The updated cloud AI can then update the edge AI model with the extended knowledge. Simulation results demonstrate that the proposed "DiReDi" framework allows the manufacturer to update the user model by learning new knowledge from the user's actual scenario with private data. The initial redundant knowledge is reduced since the retraining emphasizes user private data.