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

• URL: http://arxiv.org/abs/2406.02600v1
• Date: Sat, 1 Jun 2024 23:07:05 GMT
• Title: Data Quality in Edge Machine Learning: A State-of-the-Art Survey
• Authors: Mohammed Djameleddine Belgoumri, Mohamed Reda Bouadjenek, Sunil Aryal, Hakim Hacid,
• Abstract summary: Data-driven Artificial Intelligence (AI) systems trained using Machine Learning (ML) are shaping an ever-increasing portion of our lives. 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.
• Score: 2.8449839307925955
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. From these observations, it follows that DQ research for edge ML is a critical and urgent exploration track for the safety and robust usefulness of present and future AI systems. Despite this fact, DQ research for edge ML is still in its infancy. The literature on this subject remains fragmented and scattered across different research communities, with no comprehensive survey to date. Hence, this paper aims to fill this gap by providing a global view of the existing literature from multiple disciplines that can be grouped under the umbrella of DQ for edge ML. Specifically, we present a tentative definition of data quality in Edge computing, which we use to establish a set of DQ dimensions. We explore each dimension in detail, including existing solutions for mitigation.

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