# SplitEasy: A Practical Approach for Training ML models on Mobile Devices

arXiv: https://arxiv.org/abs/2011.04232 · HTML: https://mlsanity.com/paper/2011.04232/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2020.

## Built on by (most cited first, 10 of 10)

- [IPLS : A Framework for Decentralized Federated Learning](https://mlsanity.com/paper/2101.01901/) (2021)
- [Similarity-based Label Inference Attack against Training and Inference of Split Learning](https://mlsanity.com/paper/2203.05222/) (2022)
- [Communication and Computation Reduction for Split Learning using Asynchronous Training](https://mlsanity.com/paper/2107.09786/) (2021)
- [Training Machine Learning models at the Edge: A Survey](https://mlsanity.com/paper/2403.02619/) (2024)
- [AdaSplit: Adaptive Trade-offs for Resource-constrained Distributed Deep Learning](https://mlsanity.com/paper/2112.01637/) (2021)
- [Workflow Optimization for Parallel Split Learning](https://mlsanity.com/paper/2402.10092/) (2024)
- [MP-SL: Multihop Parallel Split Learning](https://mlsanity.com/paper/2402.00208/) (2024)
- [Federated Split Learning with Improved Communication and Storage Efficiency](https://mlsanity.com/paper/2507.15816/) (2025)
- [An Efficient Split Fine-tuning Framework for Edge and Cloud Collaborative Learning](https://mlsanity.com/paper/2211.16703/) (2022)
- [TorchFL: A Performant Library for Bootstrapping Federated Learning Experiments](https://mlsanity.com/paper/2211.00735/) (2022)

## Abstract (arXiv)

Modern mobile devices, although resourceful, cannot train state-of-the-art machine learning models without the assistance of servers, which require access to, potentially, privacy-sensitive user data. Split learning has recently emerged as a promising technique for training complex deep learning (DL) models on low-powered mobile devices. The core idea behind this technique is to train the sensitive layers of a DL model on mobile devices while offloading the computationally intensive layers to a server. Although a lot of works have already explored the effectiveness of split learning in simulated settings, a usable toolkit for this purpose does not exist. In this work, we highlight the theoretical and technical challenges that need to be resolved to develop a functional framework that trains ML models in mobile devices without transferring raw data to a server. Focusing on these challenges, we propose SplitEasy, a framework for training ML models on mobile devices using split learning. Using the abstraction provided by SplitEasy, developers can run various DL models under split learning setting by making minimal modifications. We provide a detailed explanation of SplitEasy and perform experiments with six state-of-the-art neural networks. We demonstrate how SplitEasy can train models that cannot be trained solely by a mobile device while incurring nearly constant time per data sample.