# AdaPI: Facilitating DNN Model Adaptivity for Efficient Private Inference in Edge Computing

arXiv: https://arxiv.org/abs/2407.05633 · HTML: https://mlsanity.com/paper/2407.05633/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 66 references that are in the map.
- First posted on arXiv 8 Jul 2024.

## Builds on (most cited first, 30 of 33)

- [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)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Pruning Filters for Efficient ConvNets](https://mlsanity.com/paper/1608.08710/) (2016)
- [Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer](https://mlsanity.com/paper/1612.03928/) (2016)
- [SNIP: Single-shot Network Pruning based on Connection Sensitivity](https://mlsanity.com/paper/1810.02340/) (2018)
- [Once-for-All: Train One Network and Specialize it for Efficient Deployment](https://mlsanity.com/paper/1908.09791/) (2019)
- [A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets](https://mlsanity.com/paper/1707.08819/) (2017)
- [CrypTen: Secure Multi-Party Computation Meets Machine Learning](https://mlsanity.com/paper/2109.00984/) (2021)
- [Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate](https://mlsanity.com/paper/2402.02769/) (2024)
- [CryptoNAS: Private Inference on a ReLU Budget](https://mlsanity.com/paper/2006.08733/) (2020)
- [Selective Network Linearization for Efficient Private Inference](https://mlsanity.com/paper/2202.02340/) (2022)
- [APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking](https://mlsanity.com/paper/2406.14449/) (2024)
- [LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference](https://mlsanity.com/paper/2309.14331/) (2023)
- [Pruning Algorithms to Accelerate Convolutional Neural Networks for Edge Applications: A Survey](https://mlsanity.com/paper/2005.04275/) (2020)
- [Credit Card Fraud Detection Using Advanced Transformer Model](https://mlsanity.com/paper/2406.03733/) (2024)
- [Harnessing XGBoost for Robust Biomarker Selection of Obsessive-Compulsive Disorder (OCD) from Adolescent Brain Cognitive Development (ABCD) data](https://mlsanity.com/paper/2407.00028/) (2024)
- [Learning to Linearize Deep Neural Networks for Secure and Efficient Private Inference](https://mlsanity.com/paper/2301.09254/) (2023)
- [Enhanced Detection Classification via Clustering SVM for Various Robot Collaboration Task](https://mlsanity.com/paper/2405.03026/) (2024)
- [NNSplitter: An Active Defense Solution for DNN Model via Automated Weight Obfuscation](https://mlsanity.com/paper/2305.00097/) (2023)
- [MirrorNet: A TEE-Friendly Framework for Secure On-device DNN Inference](https://mlsanity.com/paper/2311.09489/) (2023)
- [Portfolio Optimization with Robust Covariance and Conditional Value-at-Risk Constraints](https://mlsanity.com/paper/2406.00610/) (2024)
- [Rough Set improved Therapy-Based Metaverse Assisting System](https://mlsanity.com/paper/2406.04465/) (2024)
- [Characterizing and Optimizing End-to-End Systems for Private Inference](https://mlsanity.com/paper/2207.07177/) (2022)
- [Fine-Tuning Gemma-7B for Enhanced Sentiment Analysis of Financial News Headlines](https://mlsanity.com/paper/2406.13626/) (2024)
- [PASNet: Polynomial Architecture Search Framework for Two-party Computation-based Secure Neural Network Deployment](https://mlsanity.com/paper/2306.15513/) (2023)
- [Media Bias Matters: Understanding the Impact of Politically Biased News on Vaccine Attitudes in Social Media](https://mlsanity.com/paper/2403.04009/) (2024)
- [Summary Statistic Privacy in Data Sharing](https://mlsanity.com/paper/2303.02014/) (2023)
- [All-in-One: A Highly Representative DNN Pruning Framework for Edge Devices with Dynamic Power Management](https://mlsanity.com/paper/2212.05122/) (2022)
- [ObfuNAS: A Neural Architecture Search-based DNN Obfuscation Approach](https://mlsanity.com/paper/2208.08569/) (2022)

## Built on by (most cited first, 15 of 15)

- [Research on reinforcement learning based warehouse robot navigation algorithm in complex warehouse layout](https://mlsanity.com/paper/2411.06128/) (2024)
- [Cross-border Commodity Pricing Strategy Optimization via Mixed Neural Network for Time Series Analysis](https://mlsanity.com/paper/2408.12115/) (2024)
- [Deep Analysis of Time Series Data for Smart Grid Startup Strategies: A Transformer-LSTM-PSO Model Approach](https://mlsanity.com/paper/2408.12129/) (2024)
- [Advanced AI Framework for Enhanced Detection and Assessment of Abdominal Trauma: Integrating 3D Segmentation with 2D CNN and RNN Models](https://mlsanity.com/paper/2407.16165/) (2024)
- [EITNet: An IoT-Enhanced Framework for Real-Time Basketball Action Recognition](https://mlsanity.com/paper/2410.09954/) (2024)
- [LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand Forecasting](https://mlsanity.com/paper/2410.15286/) (2024)
- [Real-time Monitoring and Analysis of Track and Field Athletes Based on Edge Computing and Deep Reinforcement Learning Algorithm](https://mlsanity.com/paper/2411.06720/) (2024)
- [IoT-Based 3D Pose Estimation and Motion Optimization for Athletes: Application of C3D and OpenPose](https://mlsanity.com/paper/2411.12676/) (2024)
- [TRIZ Method for Urban Building Energy Optimization: GWO-SARIMA-LSTM Forecasting model](https://mlsanity.com/paper/2410.15283/) (2024)
- [GTA-Net: An IoT-Integrated 3D Human Pose Estimation System for Real-Time Adolescent Sports Posture Correction](https://mlsanity.com/paper/2411.06725/) (2024)
- [Optimized CNNs for Rapid 3D Point Cloud Object Recognition](https://mlsanity.com/paper/2412.02855/) (2024)
- [Intelligent logistics management robot path planning algorithm integrating transformer and GCN network](https://mlsanity.com/paper/2501.02749/) (2025)
- [Ecomap: Sustainability-Driven Optimization of Multi-Tenant DNN Execution on Edge Servers](https://mlsanity.com/paper/2503.04148/) (2025)
- [Construction and optimization of health behavior prediction model for the elderly in smart elderly care](https://mlsanity.com/paper/2412.02062/) (2024)
- [Cardiovascular Disease Detection By Leveraging Semi-Supervised Learning](https://mlsanity.com/paper/2412.10567/) (2024)

## Abstract (arXiv)

Private inference (PI) has emerged as a promising solution to execute computations on encrypted data, safeguarding user privacy and model parameters in edge computing. However, existing PI methods are predominantly developed considering constant resource constraints, overlooking the varied and dynamic resource constraints in diverse edge devices, like energy budgets. Consequently, model providers have to design specialized models for different devices, where all of them have to be stored on the edge server, resulting in inefficient deployment. To fill this gap, this work presents AdaPI, a novel approach that achieves adaptive PI by allowing a model to perform well across edge devices with diverse energy budgets. AdaPI employs a PI-aware training strategy that optimizes the model weights alongside weight-level and feature-level soft masks. These soft masks are subsequently transformed into multiple binary masks to enable adjustments in communication and computation workloads. Through sequentially training the model with increasingly dense binary masks, AdaPI attains optimal accuracy for each energy budget, which outperforms the state-of-the-art PI methods by 7.3% in terms of test accuracy on CIFAR-100. The code of AdaPI can be accessed via https://github.com/jiahuiiiiii/AdaPI.