# Adaptive AI Task Partitioning and Safe Offloading in Heterogeneous Edge-Cloud Continuum

arXiv: https://arxiv.org/abs/2605.09623 · HTML: https://mlsanity.com/paper/2605.09623/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 24 references that are in the map.
- First posted on arXiv 10 May 2026.

## Builds on (most cited first, 2 of 2)

- [Distributed Training of Deep Learning Models: A Taxonomic Perspective](https://mlsanity.com/paper/2007.03970/) (2020)
- [NeSy-Edge: Neuro-Symbolic Trustworthy Self-Healing in the Computing Continuum](https://mlsanity.com/paper/2603.21145/) (2026)

## Abstract (arXiv)

In recent years, the use of artificial intelligence on resource-constrained IoT devices has grown significantly. However, existing approaches to AI task partitioning and offloading across the edge-cloud continuum typically rely on static methods that ignore runtime dynamics. Furthermore, they are often evaluated in simulated environments rather than on real hardware. To address this gap, we propose a framework that dynamically splits neural network layers across the heterogeneous continuum. The framework profiles the model at startup, measures network link conditions between nodes, and periodically re-evaluates the partition to adapt to environmental changes. We created a physical testbed comprising a Raspberry Pi edge device, a laptop fog, and a high-performance desktop PC as the cloud. We evaluated the framework over three widely adopted convolutional neural networks: VGG16, AlexNet, and MobileNetV2. Our results show that the framework achieves reductions in energy and end-to-end latency of 27.09--35.82% and 6.34--22.92%, respectively, compared to a static partitioning baseline. These findings confirm the superiority of adaptive to static partitioning.