ProtoGuard-SL: Prototype Consistency Based Backdoor Defense for Vertical Split Learning

Computer Science > Cryptography and Security [Submitted on 4 Apr 2026] ProtoGuard-SL: Prototype Consistency Based Backdoor Defense for Vertical Split Learning Yuhan Shui, Ruobin Jin, Zhihao Dou, Zhiqiang Gao Vertical split learning (SL) enables collaborative model training across parties holding complementary features without sharing raw data, but recent work has shown that it is highly vulnerable to poisoning-based backdoor attacks operating on intermediate embeddings. By compromising malicious clients, adversaries can inject stealthy triggers that manipulate the server-side model while remaining difficult to detect, and existing defenses provide limited robustness against adaptive attacks. In this paper, we propose ProtoGuard-SL, a server-side defense that improves the robustness of split learning by exploiting class-conditional representation consistency in the embedding space. Our approach is motivated by the observation that benign embeddings within the same class exhibit stable semantic alignment, whereas poisoned embeddings inevitably disrupt this structure. ProtoGuard-SL adopts a two-stage framework that constructs robust class prototypes and transforms embeddings into a prototype-consistency representation, followed by a class-conditional, distribution-free conformal filtering strategy to identify and remove anomalous embeddings. Extensive experiments are conducted on three datasets, CIFAR-10, SVHN, and Bank Marketing, under three different attack settings demonstrate that our method achieves state-of-the-art performance. Comments: ICME 2026 Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2604.03595 [cs.CR]   (or arXiv:2604.03595v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2604.03595 Focus to learn more Submission history From: Zhihao Dou [view email] [v1] Sat, 4 Apr 2026 05:24:09 UTC (258 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)