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arXiv · 2505.05872

A Taxonomy of Attacks and Defenses in Split Learning

Aqsa Shabbir·Halil İbrahim Kanpak·Alptekin Küpçü·Sinem Sav
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Abstract

Split Learning (SL) has emerged as a promising paradigm for distributed deep learning, allowing resource-constrained clients to offload portions of their model computation to servers while maintaining collaborative learning. However, recent research has demonstrated that SL remains vulnerable to a range of privacy and security threats, including information leakage, model inversion, and adversarial attacks. While various defense mechanisms have been proposed, a systematic understanding of the attack landscape and corresponding countermeasures is still lacking. In this study, we present a comprehensive taxonomy of attacks and defenses in SL, categorizing them along three key dimensions: employed strategies, constraints, and effectiveness. Furthermore, we identify key open challenges and research gaps in SL based on our systematization, highlighting potential future directions.

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Aqsa Shabbir, Halil İbrahim Kanpak, Alptekin Küpçü, Sinem Sav. 2025-05-09. A Taxonomy of Attacks and Defenses in Split Learning. https://arxiv.org/abs/2505.05872

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