Certified Streaming Updates for Machine Learning Models under Limited Throughput.

Authors

Dongxie Wen, Lu Yi, Fengtai Li, Shaoqiu Zhang, Xiao Zhang, Zhewei Wei

Published in

IEEE transactions on pattern analysis and machine intelligence. Volume PP. Aug 26, 2026. Epub Aug 26, 2026.

Abstract

The rapid growth of real-time data acquisition creates a critical demand for streaming machine learning at the edge to enhance application performance; however, the sheer magnitude of incoming data often overwhelms the modest computational resources of edge hardware. Consider autonomous vehicle condition recognition, where sensors generate high frequency signals at sub-second intervals, yet edge computing resources can process only a fraction of the incoming data stream. This raises the fundamental question of how to balance the trade-off between throughput constraints, update efficiency, and model utility. In this paper, we introduce Scalable Streaming Learning via online Sampling (S3), an efficient model-updating framework designed to handle limited-throughput streaming data while theoretically guaranteeing an approximation to the complete retrained model. S3 utilizes online sampling to construct throughput-admissible subsets from the continuous data stream, performing Newton-step updates on these representative samples. Through rigorous theoretical analysis, we demonstrate that S3 provides certified streaming updates under both uniform and leverage-based sampling strategies. Experiments on real-world datasets, including vehicle condition recognition, validate our theoretical guarantees and demonstrate practical effectiveness.

PMID:
42647714
Bibliographic data and abstract were imported from PubMed on 27 Aug 2026.

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