Summary of Delta: a Cloud-assisted Data Enrichment Framework For On-device Continual Learning, by Chen Gong et al.
Delta: A Cloud-assisted Data Enrichment Framework for On-Device Continual Learning
by Chen Gong, Zhenzhe Zheng, Fan Wu, Xiaofeng Jia, Guihai Chen
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 paper presents Delta, a private, efficient, and effective data enrichment framework for on-device continual learning (CL) in mobile applications. Existing research focuses on lightweight CL frameworks, but the authors identify data scarcity as a critical bottleneck. Delta leverages cloud-side data to enrich scarce on-device data without sharing sensitive information. The framework proposes a soft data matching strategy for device-side sub-problems and an optimal data sampling scheme for cloud servers with low computational complexity. Additionally, Delta refines the data sampling scheme by considering the impact of enriched data on both new and past contexts, mitigating catastrophic forgetting. Comprehensive experiments demonstrate that Delta enhances model accuracy by an average of 15.1%, 12.4%, 1.1%, and 5.6% for visual, IMU, audio, and textual tasks compared to few-shot CL, while reducing communication costs by over 90% compared to federated CL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine your phone learning new things without needing to send all the data to a cloud server. This paper proposes a way to make that happen. They want to help mobile apps learn and improve in different situations, like when you’re using a camera or listening to music. The main problem is that there’s not enough data available on your device. So, they developed a system called Delta that uses cloud data to enrich the limited data on your phone without sharing sensitive information. This makes learning faster and more accurate. They tested it with different types of data and found that it improved accuracy by 15-20% compared to other methods, while reducing the amount of data sent to the cloud server. |
Keywords
» Artificial intelligence » Continual learning » Few shot
