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

• URL: http://arxiv.org/abs/2507.17228v2
• Date: Tue, 05 Aug 2025 02:08:01 GMT
• Title: P3SL: Personalized Privacy-Preserving Split Learning on Heterogeneous Edge Devices
• Authors: Wei Fan, JinYi Yoon, Xiaochang Li, Huajie Shao, Bo Ji,
• Abstract summary: Split Learning (SL) enables resource constrained edge devices to participate in model training by partitioning a model into client-side and server-side sub-models.<n>SL encounters significant challenges in heterogeneous environments where devices vary in computing resources, communication capabilities, environmental conditions, and privacy requirements.<n>We propose P3SL, a Personalized Privacy-Preserving Split Learning framework designed for heterogeneous, resource-constrained edge device systems.
• Score: 12.821321451464081
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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