SOURCE-LINKED INTELLIGENCE

TASTE: Throughput-Aware Batch Size Tuning for On-Device Edge Learning

arXiv · AI, language, vision and robotics · article · Sep 7, 2026 · UTC

The rise of privacy-preserving artificial intelligence (AI) has shifted the focus of model adaptation and personalization towards on-device learning, where deep learning models are finetuned directly on edge hardware using local user data. However, this shift requires optimization of deep learning training on resource-constrained hardware to maximize throughput while maintaining predictive accuracy. This paper introduces a novel technique for on-device model training that incorporates an efficient Bayesian optimization-based batch size tuning approach to maximize hardware throughput. To evalua

Read original source ↗ Open in workspace

recordType

paper

region

Global