Summary of Diredi: Distillation and Reverse Distillation For Aiot Applications, by Chen Sun et al.
DiReDi: Distillation and Reverse Distillation for AIoT Applications
by Chen Sun, Qing Tong, Wenshuang Yang, Wenqi Zhang
First submitted to arxiv on: 12 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
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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 paper proposes a novel framework, DiReD, for deploying edge AI models in various real-world scenarios while addressing customization and extension challenges. Edge AI models are initially trained with presumed data using cloud AI models and then deployed for inference on edge devices. When users need to update the model to better fit their scenario, the reverse distillation process extracts knowledge from user-exclusive data, protecting privacy by not sharing exclusive data. The extracted knowledge is reported back to the cloud server to update the cloud AI model, which can then update the edge AI model with extended knowledge. Simulation results demonstrate that DiReD enables manufacturers to learn new knowledge from users’ actual scenarios without compromising privacy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a world where artificial intelligence (AI) can be used in many different ways, like in smart homes or self-driving cars. But right now, it’s hard for people to customize AI models to fit their specific needs. This paper proposes a new way to do this called DiReD. It lets people use AI models on their own devices and then updates the model based on what they actually need, without sharing private information. This is important because it keeps user data safe while still allowing for personalized AI experiences. |
Keywords
» Artificial intelligence » Distillation » Inference
