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DINI: Data Imputation Using Neural Inversion for Edge Applications

Author(s): Tuli, Shikhar; Jha, Niraj K

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Abstract: The edge computing paradigm has recently drawn significant attention from industry and academia. Due to the advantages in quality-of-service metrics, namely, latency, bandwidth, energy efficiency, privacy, and security, deploying artificial intelligence (AI) models at the network edge has attracted widespread interest. Edge-AI has seen applications in diverse domains that involve large amounts of data. However, poor dataset quality plagues this compute regime owing to numerous data corruption sources, including missing data. As such systems are increasingly being deployed in mission-critical applications, mitigating the effects of corrupted data becomes important. In this work, we propose a strategy based on data imputation using neural inversion, DINI. It trains a surrogate model and runs data imputation in an interleaved fashion. Unlike previous works, DINI is a model-agnostic framework applicable to diverse deep learning architectures. DINI outperforms state-of-the-art methods by at least 10.7% in average imputation error. Applying DINI to mission-critical applications can increase prediction accuracy to up to 99% (F1 score of 0.99), resulting in significant gains compared to baseline methods.
Publication Date: 23-Nov-2022
Citation: Tuli, Shikhar, Jha, Niraj K. (DINI: data imputation using neural inversion for edge applications. Scientific Reports, 12 (10.1038/s41598-022-24369-1
DOI: doi:10.1038/s41598-022-24369-1
Type of Material: Journal Article
Journal/Proceeding Title: Nature Scientific Reports
Version: Final published version. This is an open access article.

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