Optimizing Split Learning Latency in TinyML-Based IoT Systems

arXiv:2507.16594v2 Announce Type: replace-cross Abstract: Split learning (SL) addresses the limitation of running deep learning inference directly on low-power edge/IoT nodes, in which it executes part of the inference process on the sensor and offloading the remainder to a companion device. Despite its promise, the inference latency of SL on constrained hardware under realistic low-power wireless protocols remains unexplored. This paper presents the first experimental latency benchmark of TinyM