Summary of Expand Heterogeneous Learning Systems with Selective Multi-source Knowledge Fusion, by Gaole Dai et al.
Expand Heterogeneous Learning Systems with Selective Multi-Source Knowledge Fusion
by Gaole Dai, Huatao Xu, Yifan Yang, Rui Tan, Mo Li
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 The proposed framework, HaT, aims to expand learning systems by providing high-quality customized models for various domains, including new users. The challenge lies in overcoming limited labeled data and data-device heterogeneities. Knowledge distillation methods can address label scarcity and device heterogeneity but assume reliable teachers and neglect data heterogeneity. To address this issue, HaT selects multiple high-quality models from the system at a low cost, fuses their knowledge by assigning sample-wise weights to predictions, and selectively injects fused knowledge into customized models based on quality. Experimental results demonstrate that HaT outperforms state-of-the-art baselines by up to 16.5% accuracy and saves up to 39% communication traffic. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary HaT is a new way to make learning systems better for more people. Right now, it’s hard to get good models for new users because there isn’t enough labeled data and devices are very different. Some methods can help with this problem, but they assume that the teachers are perfect and don’t account for differences in data. HaT solves this by picking the best models from a system at low cost, combining their knowledge, and adding it to customized models based on quality. This makes learning systems better and more efficient. |
Keywords
» Artificial intelligence » Knowledge distillation
