# Workflow Optimization for Parallel Split Learning

arXiv: https://arxiv.org/abs/2402.10092 · HTML: https://mlsanity.com/paper/2402.10092/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 50 references that are in the map.
- First posted on arXiv 1 Feb 2024.

## Builds on (most cited first, 19 of 19)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [SplitFed: When Federated Learning Meets Split Learning](https://mlsanity.com/paper/2004.12088/) (2020)
- [Model Pruning Enables Efficient Federated Learning on Edge Devices](https://mlsanity.com/paper/1909.12326/) (2019)
- [Split Learning over Wireless Networks: Parallel Design and Resource Management](https://mlsanity.com/paper/2204.08119/) (2022)
- [Extremely Low Bit Neural Network: Squeeze the Last Bit Out with ADMM](https://mlsanity.com/paper/1707.09870/) (2017)
- [A Bargaining Game for Personalized, Energy Efficient Split Learning over Wireless Networks](https://mlsanity.com/paper/2212.06107/) (2022)
- [Privacy and Efficiency of Communications in Federated Split Learning](https://mlsanity.com/paper/2301.01824/) (2023)
- [Federated Learning under Heterogeneous and Correlated Client Availability](https://mlsanity.com/paper/2301.04632/) (2023)
- [Communication-Efficient Federated Learning for Heterogeneous Edge Devices Based on Adaptive Gradient Quantization](https://mlsanity.com/paper/2212.08272/) (2022)
- [SplitEasy: A Practical Approach for Training ML models on Mobile Devices](https://mlsanity.com/paper/2011.04232/) (2020)
- [Optimal Task Assignment to Heterogeneous Federated Learning Devices](https://mlsanity.com/paper/2010.00239/) (2020)
- [A General System for Heuristic Solution of Convex Problems over Nonconvex Sets](https://mlsanity.com/paper/1601.07277/) (2016)
- [DNNAbacus: Toward Accurate Computational Cost Prediction for Deep Neural Networks](https://mlsanity.com/paper/2205.12095/) (2022)
- [Adaptive Control of Client Selection and Gradient Compression for Efficient Federated Learning](https://mlsanity.com/paper/2212.09483/) (2022)

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

- [Hierarchical Split Federated Learning: Convergence Analysis and System Optimization](https://mlsanity.com/paper/2412.07197/) (2024)
- [Collaborative Split Federated Learning with Parallel Training and Aggregation](https://mlsanity.com/paper/2504.15724/) (2025)
- [Data Heterogeneity and Forgotten Labels in Split Federated Learning](https://mlsanity.com/paper/2511.09736/) (2025)
- [ASFL: An Adaptive Model Splitting and Resource Allocation Framework for Split Federated Learning](https://mlsanity.com/paper/2603.04437/) (2026)
- [Integration of TinyML and LargeML: A Survey of 6G and Beyond](https://mlsanity.com/paper/2505.15854/) (2025)
- [Makespan Minimization in Split Learning: From Theory to Practice](https://mlsanity.com/paper/2602.06693/) (2026)
- [Split Federated Learning Architectures for High-Accuracy and Low-Delay Model Training](https://mlsanity.com/paper/2603.08687/) (2026)
- [Equitable Multi-Task Learning for AI-RANs](https://mlsanity.com/paper/2603.08717/) (2026)

## Abstract (arXiv)

Split learning (SL) has been recently proposed as a way to enable resource-constrained devices to train multi-parameter neural networks (NNs) and participate in federated learning (FL). In a nutshell, SL splits the NN model into parts, and allows clients (devices) to offload the largest part as a processing task to a computationally powerful helper. In parallel SL, multiple helpers can process model parts of one or more clients, thus, considerably reducing the maximum training time over all clients (makespan). In this paper, we focus on orchestrating the workflow of this operation, which is critical in highly heterogeneous systems, as our experiments show. In particular, we formulate the joint problem of client-helper assignments and scheduling decisions with the goal of minimizing the training makespan, and we prove that it is NP-hard. We propose a solution method based on the decomposition of the problem by leveraging its inherent symmetry, and a second one that is fully scalable. A wealth of numerical evaluations using our testbed's measurements allow us to build a solution strategy comprising these methods. Moreover, we show that this strategy finds a near-optimal solution, and achieves a shorter makespan than the baseline scheme by up to 52.3%.