StreamFP: Learnable Fingerprint-guided Data Selection for Efficient Stream Learning
This addresses the challenge of balancing computational efficiency and accuracy in stream learning for applications with fast-evolving data, representing a strong specific gain rather than a foundational advance.
The paper tackled the problem of efficient data selection in stream learning by proposing StreamFP, a method using learnable fingerprints, which achieved accuracy improvements of up to 51.24% and a 4.6x increase in training throughput compared to baselines.
Stream Learning (SL) requires models that can quickly adapt to continuously evolving data, posing significant challenges in both computational efficiency and learning accuracy. Effective data selection is critical in SL to ensure a balance between information retention and training efficiency. Traditional rule-based data selection methods struggle to accommodate the dynamic nature of streaming data, highlighting the necessity for innovative solutions that effectively address these challenges. Recent approaches to handling changing data distributions face challenges that limit their effectiveness in fast-paced environments. In response, we propose StreamFP, a novel approach that uniquely employs dynamic, learnable parameters called fingerprints to enhance data selection efficiency and adaptability in stream learning. StreamFP optimizes coreset selection through its unique fingerprint-guided mechanism for efficient training while ensuring robust buffer updates that adaptively respond to data dynamics, setting it apart from existing methods in stream learning. Experimental results demonstrate that StreamFP outperforms state-of-the-art methods by achieving accuracy improvements of 15.99%, 29.65%, and 51.24% compared to baseline models across varying data arrival rates, alongside a training throughput increase of 4.6x.