Summary of Adapi: Facilitating Dnn Model Adaptivity For Efficient Private Inference in Edge Computing, by Tong Zhou and Jiahui Zhao and Yukui Luo and Xi Xie and Wujie Wen and Caiwen Ding and Xiaolin Xu
AdaPI: Facilitating DNN Model Adaptivity for Efficient Private Inference in Edge Computing
by Tong Zhou, Jiahui Zhao, Yukui Luo, Xi Xie, Wujie Wen, Caiwen Ding, Xiaolin Xu
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research presents AdaPI, a novel approach to private inference (PI) that enables models to perform well across edge devices with diverse energy budgets. Existing PI methods are limited by constant resource constraints, leading to inefficient deployment and the need for specialized models for each device. AdaPI employs a PI-aware training strategy that optimizes model weights alongside soft masks, which are transformed into binary masks to adjust communication and computation workloads. Through sequential training with increasingly dense binary masks, AdaPI achieves optimal accuracy for each energy budget, outperforming state-of-the-art PI methods by 7.3% on CIFAR-100. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a way to help computers learn from data without sharing the data itself. This is important because it keeps people’s personal information safe and lets computers work together in a secure way. The problem is that different devices have different amounts of power, which can make it hard for computers to work together. The new method, called AdaPI, makes sure computers can learn from data on different devices without using too much power. This means they can work better together and keep people’s information safe. |
Keywords
» Artificial intelligence » Inference
