# HierTrain: Fast Hierarchical Edge AI Learning with Hybrid Parallelism in Mobile-Edge-Cloud Computing

arXiv: https://arxiv.org/abs/2003.09876 · HTML: https://mlsanity.com/paper/2003.09876/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2020.

## Built on by (most cited first, 8 of 8)

- [Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2404.06114/) (2024)
- [An Overview on Generative AI at Scale with Edge-Cloud Computing](https://mlsanity.com/paper/2306.17170/) (2023)
- [Hierarchical Training of Deep Neural Networks Using Early Exiting](https://mlsanity.com/paper/2303.02384/) (2023)
- [A Survey on Cloud-Edge-Terminal Collaborative Intelligence in AIoT Networks](https://mlsanity.com/paper/2508.18803/) (2025)
- [DynaComm: Accelerating Distributed CNN Training between Edges and Clouds through Dynamic Communication Scheduling](https://mlsanity.com/paper/2101.07968/) (2021)
- [FTPipeHD: A Fault-Tolerant Pipeline-Parallel Distributed Training Framework for Heterogeneous Edge Devices](https://mlsanity.com/paper/2110.02781/) (2021)
- [AccEPT: An Acceleration Scheme for Speeding Up Edge Pipeline-parallel Training](https://mlsanity.com/paper/2311.05827/) (2023)
- [Design and Optimization of Hierarchical Gradient Coding for Distributed Learning at Edge Devices](https://mlsanity.com/paper/2406.10831/) (2024)

## Abstract (arXiv)

Nowadays, deep neural networks (DNNs) are the core enablers for many emerging edge AI applications. Conventional approaches to training DNNs are generally implemented at central servers or cloud centers for centralized learning, which is typically time-consuming and resource-demanding due to the transmission of a large amount of data samples from the device to the remote cloud. To overcome these disadvantages, we consider accelerating the learning process of DNNs on the Mobile-Edge-Cloud Computing (MECC) paradigm. In this paper, we propose HierTrain, a hierarchical edge AI learning framework, which efficiently deploys the DNN training task over the hierarchical MECC architecture. We develop a novel textithybrid parallelism method, which is the key to HierTrain, to adaptively assign the DNN model layers and the data samples across the three levels of edge device, edge server and cloud center. We then formulate the problem of scheduling the DNN training tasks at both layer-granularity and sample-granularity. Solving this optimization problem enables us to achieve the minimum training time. We further implement a hardware prototype consisting of an edge device, an edge server and a cloud server, and conduct extensive experiments on it. Experimental results demonstrate that HierTrain can achieve up to 6.9x speedup compared to the cloud-based hierarchical training approach.