Oops!... They Stole it Again: Attacks on Split Learning
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Abstract (arXiv)
Split Learning (SL) is a collaborative learning approach that improves privacy by keeping data on the client-side while sharing only the intermediate output with a server. However, the distributed nature of SL introduces new security challenges, necessitating a comprehensive exploration of potential attacks. This paper systematically reviews various attacks on SL, classifying them based on factors such as the attacker's role, the type of privacy risks, when data leaks occur, and where vulnerabilities exist. We also analyze existing defense methods, including cryptographic methods, data modification approaches, distributed techniques, and hybrid solutions. Our findings reveal security gaps, highlighting the effectiveness and limitations of existing defenses. By identifying open challenges and future directions, this work provides valuable information to improve SL privacy issues and guide further research.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 45 of its 98 references that are in the map. First posted on arXiv 14 Aug 2025.
Builds on
45 of 98 references.
References · 45
- 2020SplitFed: When Federated Learning Meets Split Learningcited by 310
- 2018Distributed learning of deep neural network over multiple agentscited by 279
- 2021Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challengescited by 115
- 2020Unleashing the Tiger: Inference Attacks on Split Learningcited by 105
- 2021Label Leakage and Protection in Two-party Split Learningcited by 90
- 2021UnSplit: Data-Oblivious Model Inversion, Model Stealing, and Label Inference Attacks Against Split Learningcited by 52
- 2020Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?cited by 50
- 2022ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learningcited by 36
- 2020Advancements of federated learning towards privacy preservation: from federated learning to split learningcited by 34
- 2021Practical Defences Against Model Inversion Attacks for Split Neural Networkscited by 29
- 2022Differentially Private Label Protection in Split Learningcited by 26
- 2020FedSL: Federated Split Learning on Distributed Sequential Data in Recurrent Neural Networkscited by 23
33 more
- 2020DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networkscited by 23
- 2023Reducing Communication for Split Learning by Randomized Top-k Sparsificationcited by 22
- 2021SplitGuard: Detecting and Mitigating Training-Hijacking Attacks in Split Learningcited by 20
- 2024A Stealthy Wrongdoer: Feature-Oriented Reconstruction Attack against Split Learningcited by 17
- 2021ExPLoit: Extracting Private Labels in Split Learningcited by 15
- 2022Feature Space Hijacking Attacks against Differentially Private Split Learningcited by 15
- 2022Similarity-based Label Inference Attack against Training and Inference of Split Learningcited by 15
- 2023Split Ways: Privacy-Preserving Training of Encrypted Data Using Split Learningcited by 15
- 2022Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directionscited by 14
- 2024Vertical Federated Learning for Effectiveness, Security, Applicability: A Surveycited by 14
- 2021Blind Faith: Privacy-Preserving Machine Learning using Function Approximationcited by 10
- 2022HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learningcited by 10
- 2023Label Inference Attack against Split Learning under Regression Settingcited by 8
- 2023Secure Split Learning against Property Inference, Data Reconstruction, and Feature Space Hijacking Attackscited by 8
- 2022Get your Foes Fooled: Proximal Gradient Split Learning for Defense against Model Inversion Attacks on IoMT datacited by 7
- 2024GuardML: Efficient Privacy-Preserving Machine Learning Services Through Hybrid Homomorphic Encryptioncited by 7
- 2023On Feasibility of Server-side Backdoor Attacks on Split Learningcited by 6
- 2023Split Without a Leak: Reducing Privacy Leakage in Split Learningcited by 6
- 2023Learning in the Dark: Privacy-Preserving Machine Learning using Function Approximationcited by 6
- 2024Make Split, not Hijack: Preventing Feature-Space Hijacking Attacks in Split Learningcited by 6
- 2024CURE: Privacy-Preserving Split Learning Done Rightcited by 6
- 2024An Efficient Privacy-aware Split Learning Framework for Satellite Communicationscited by 6
- 2021Unsupervised Information Obfuscation for Split Inference of Neural Networkscited by 5
- 2022Security Analysis of SplitFed Learningcited by 5
- 2023A More Secure Split: Enhancing the Security of Privacy-Preserving Split Learningcited by 5
- 2022Federated Split GANscited by 4
- 2023Analyzing the vulnerabilities in SplitFed Learning: Assessing the robustness against Data Poisoning Attackscited by 4
- 2023SLPerf: a Unified Framework for Benchmarking Split Learningcited by 3
- 2023MISA: Unveiling the Vulnerabilities in Split Federated Learningcited by 3
- 2019Distributed Layer-Partitioned Training for Privacy-Preserved Deep Learningcited by 2
- 2023Defending Label Inference Attacks in Split Learning under Regression Settingcited by 2
- 2024Training on Fake Labels: Mitigating Label Leakage in Split Learning via Secure Dimension Transformationcited by 2
- 2024Edge-Only Universal Adversarial Attacks in Distributed Learningcited by 2
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Citing papers per year: 2026: 1.
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