Summary of Data Quality in Edge Machine Learning: a State-of-the-art Survey, by Mohammed Djameleddine Belgoumri et al.
Data Quality in Edge Machine Learning: A State-of-the-Art Survey
by Mohammed Djameleddine Belgoumri, Mohamed Reda Bouadjenek, Sunil Aryal, Hakim Hacid
First submitted to arxiv on: 1 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 paper aims to provide a comprehensive survey on Data Quality (DQ) research for edge Machine Learning (ML), considering the proliferation of Edge Computing and Internet of Things devices that train and deploy ML models. The authors highlight the importance of establishing high-quality standards, as AI systems trained using ML have significant influence on various aspects of life. They identify data-related issues in edge environments, characterized by limited resources, decentralized storage, and processing, making DQ research for edge ML an urgent exploration track for ensuring safety and robustness. The paper defines data quality in Edge computing and establishes a set of DQ dimensions, exploring each dimension in detail, including existing solutions for mitigation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how artificial intelligence systems trained using machine learning are becoming more important in our lives. It talks about the importance of having good-quality data to train these systems. With many devices connected to the internet and storing data, it can be hard to make sure this data is reliable and accurate. The authors think that finding ways to improve data quality for edge computing is a crucial area of research. |
Keywords
» Artificial intelligence » Machine learning
