# P3SL: Personalized Privacy-Preserving Split Learning on Heterogeneous Edge Devices

arXiv: https://arxiv.org/abs/2507.17228 · HTML: https://mlsanity.com/paper/2507.17228/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 47 references that are in the map.
- First posted on arXiv 23 Jul 2025.

## Builds on (most cited first, 27 of 27)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Deep Leakage from Gradients](https://mlsanity.com/paper/1906.08935/) (2019)
- [Membership Inference Attacks From First Principles](https://mlsanity.com/paper/2112.03570/) (2021)
- [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)
- [Distributed learning of deep neural network over multiple agents](https://mlsanity.com/paper/1810.06060/) (2018)
- [Reconstructing Training Data from Trained Neural Networks](https://mlsanity.com/paper/2206.07758/) (2022)
- [A Review on Bilevel Optimization: From Classical to Evolutionary Approaches and Applications](https://mlsanity.com/paper/1705.06270/) (2017)
- [Unleashing the Tiger: Inference Attacks on Split Learning](https://mlsanity.com/paper/2012.02670/) (2020)
- [Label Leakage and Protection in Two-party Split Learning](https://mlsanity.com/paper/2102.08504/) (2021)
- [Split Learning in 6G Edge Networks](https://mlsanity.com/paper/2306.12194/) (2023)
- [End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things](https://mlsanity.com/paper/2003.13376/) (2020)
- [UnSplit: Data-Oblivious Model Inversion, Model Stealing, and Label Inference Attacks Against Split Learning](https://mlsanity.com/paper/2108.09033/) (2021)
- [Advancements of federated learning towards privacy preservation: from federated learning to split learning](https://mlsanity.com/paper/2011.14818/) (2020)
- [Practical Defences Against Model Inversion Attacks for Split Neural Networks](https://mlsanity.com/paper/2104.05743/) (2021)
- [Server-Side Local Gradient Averaging and Learning Rate Acceleration for Scalable Split Learning](https://mlsanity.com/paper/2112.05929/) (2021)
- [DeepIoT: Compressing Deep Neural Network Structures for Sensing Systems with a Compressor-Critic Framework](https://mlsanity.com/paper/1706.01215/) (2017)
- [On-device Training: A First Overview on Existing Systems](https://mlsanity.com/paper/2212.00824/) (2022)
- [Constrained Bi-Level Optimization: Proximal Lagrangian Value function Approach and Hessian-free Algorithm](https://mlsanity.com/paper/2401.16164/) (2024)
- [Asteroid: Resource-Efficient Hybrid Pipeline Parallelism for Collaborative DNN Training on Heterogeneous Edge Devices](https://mlsanity.com/paper/2408.08015/) (2024)
- [Get your Foes Fooled: Proximal Gradient Split Learning for Defense against Model Inversion Attacks on IoMT data](https://mlsanity.com/paper/2201.04569/) (2022)
- [CURE: Privacy-Preserving Split Learning Done Right](https://mlsanity.com/paper/2407.08977/) (2024)
- [Split Learning without Local Weight Sharing to Enhance Client-side Data Privacy](https://mlsanity.com/paper/2212.00250/) (2022)
- [Adaptive Split Learning over Energy-Constrained Wireless Edge Networks](https://mlsanity.com/paper/2403.05158/) (2024)
- [Split Federated Learning Over Heterogeneous Edge Devices: Algorithm and Optimization](https://mlsanity.com/paper/2411.13907/) (2024)
- [Resource-efficient Parallel Split Learning in Heterogeneous Edge Computing](https://mlsanity.com/paper/2403.15815/) (2024)

## Built on by (most cited first, 2 of 2)

- [JanusVLN: Decoupling Semantics and Spatiality with Dual Implicit Memory for Vision-Language Navigation](https://mlsanity.com/paper/2509.22548/) (2025)
- [Communication-Efficient and Interoperable Distributed Learning](https://mlsanity.com/paper/2509.22823/) (2025)

## Abstract (arXiv)

Split Learning (SL) is an emerging privacy-preserving machine learning technique that enables resource constrained edge devices to participate in model training by partitioning a model into client-side and server-side sub-models. While SL reduces computational overhead on edge devices, it encounters significant challenges in heterogeneous environments where devices vary in computing resources, communication capabilities, environmental conditions, and privacy requirements. Although recent studies have explored heterogeneous SL frameworks that optimize split points for devices with varying resource constraints, they often neglect personalized privacy requirements and local model customization under varying environmental conditions. To address these limitations, we propose P3SL, a Personalized Privacy-Preserving Split Learning framework designed for heterogeneous, resource-constrained edge device systems. The key contributions of this work are twofold. First, we design a personalized sequential split learning pipeline that allows each client to achieve customized privacy protection and maintain personalized local models tailored to their computational resources, environmental conditions, and privacy needs. Second, we adopt a bi-level optimization technique that empowers clients to determine their own optimal personalized split points without sharing private sensitive information (i.e., computational resources, environmental conditions, privacy requirements) with the server. This approach balances energy consumption and privacy leakage risks while maintaining high model accuracy. We implement and evaluate P3SL on a testbed consisting of 7 devices including 4 Jetson Nano P3450 devices, 2 Raspberry Pis, and 1 laptop, using diverse model architectures and datasets under varying environmental conditions.