TASTE: Throughput-Aware Batch Size Tuning for On-Device Edge Learning
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 evaluate the impact of this hyperparameter on the learning dynamics, we investigated two distinct parad...
| Authors | Avik Bhatnagar, Federico Nicolas Peccia, Oliver Bringmann |
|---|---|
| Primary category | cs.LG |
| Published | 7 September 2026 |
| Last updated at source | 7 September 2026 |
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