The confidentiality of trained AI models on edge devices is at risk from side-channel attacks exploiting power and electromagnetic emissions. This paper proposes a novel training methodology to enhance resilience against such threats by introducing randomized and interchangeable model configurations during inference. Experimental results on Google Coral Edge TPU show a reduction in side-channel leakage and a slower increase in t-scores over 20,000 traces, demonstrating robustness against adversarial observations. The defense maintains high accuracy, with about 1% degradation in most configurations, and requires no additional hardware or software changes, making it the only applicable solution for existing Edge TPUs.
@article{arxiv.2506.06597,
title = {Stochastic Training for Side-Channel Resilient AI},
author = {Anuj Dubey and Aydin Aysu},
journal= {arXiv preprint arXiv:2506.06597},
year = {2025}
}