Fragility-Aware Noise Training for Robust Split Computing over Noisy Channels

Split computing (SC) partitions deep neural networks (DNNs) between edge and cloud devices, reducing edge-side computation while leveraging powerful cloud resources. However, in practice, intermediate features must traverse non-ideal communication links, where diverse perturbations can corrupt task-relevant representations and substantially degrade inference performance. To address this challenge, we develop a biologically inspired SC framework that progresses hierarchically from peripheral perception through neural transmission to central cognition, and further propose Fragility-Aware Noise Training (FNT). FNT estimates a relative fragility field over intermediate features, converts it into a bounded local SNR field, and allocates stronger but controlled perturbations to fragile locations under a min–max objective, encouraging the split model to learn more robust intermediate representations. Extensive experiments across diverse tasks, architectures, and split points show that FNT consistently improves robustness, achieving an average accuracy gain of 8.75% over the strongest baseline and a gain of 36.27% under the strongest additive perturbations. Moreover, under cross-noise evaluation, FNT retains strong cross-noise robustness, improving average accuracy by 10.75% and the performance under the strongest unseen perturbations by 28.19%. These results demonstrate that FNT improves the robustness of intermediate-feature transmission under diverse and unmatched channel perturbations.