Data Quality in Edge Machine Learning: A State-of-the-Art Survey

Understand

Data-driven Artificial Intelligence (AI) systems trained using Machine Learning (ML) are shaping an ever-increasing (in size and importance) portion of our lives, including, but not limited to, recommendation systems, autonomous driving technologies, healthcare diagnostics, financial services, and personalized marketing.

• On the one hand, the outsized influence of these systems imposes a high standard of quality, particularly in the data used to train them.
• On the other hand, establishing and maintaining standards of Data Quality (DQ) becomes more challenging due to the proliferation of Edge Computing and Internet of Things devices, along with their increasing adoption for training and deploying ML models.
• The nature of the edge environment -- characterized by limited resources, decentralized data storage, and processing -- exacerbates data-related issues, making them more frequent, severe, and difficult to detect and mitigate.