Verifiable Split Learning via zk-SNARKs
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Abstract (arXiv)
Split learning is an approach to collaborative learning in which a deep neural network is divided into two parts: client-side and server-side at a cut layer. The client side executes its model using its raw input data and sends the intermediate activation to the server side. This configuration architecture is very useful for enabling collaborative training when data or resources are separated between devices. However, split learning lacks the ability to verify the correctness and honesty of the computations that are performed and exchanged between the parties. To this purpose, this paper proposes a verifiable split learning framework that integrates a zk-SNARK proof to ensure correctness and verifiability. The zk-SNARK proof and verification are generated for both sides in forward propagation and backward propagation on the server side, guaranteeing verifiability on both sides. The verifiable split learning architecture is compared to a blockchain-enabled system for the same deep learning network, one that records updates but without generating the zero-knowledge proof. From the comparison, it can be deduced that applying the zk-SNARK test achieves verifiability and correctness, while blockchains are lightweight but unverifiable.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 19 references that are in the map. First posted on arXiv 3 Nov 2025.
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12 of 19 references.
References · 12
- 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 391
- 2018Distributed learning of deep neural network over multiple agentscited by 277
- 2020Unleashing the Tiger: Inference Attacks on Split Learningcited by 104
- 2023martFL: Enabling Utility-Driven Data Marketplace with a Robust and Verifiable Federated Learning Architecturecited by 11
- 2022A Review of zk-SNARKscited by 8
- 2023Love or Hate? Share or Split? Privacy-Preserving Training Using Split Learning and Homomorphic Encryptioncited by 8
- 2023Enhancing Accuracy-Privacy Trade-off in Differentially Private Split Learningcited by 8
- 2024Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learningcited by 8
- 2025Zero-Knowledge Proof Frameworks: A Systematic Surveycited by 5
- 2025VerifBFL: Leveraging zk-SNARKs for A Verifiable Blockchained Federated Learningcited by 4
- 2025Towards Privacy-Preserving Split Learning: Destabilizing Adversarial Inference and Reconstruction Attacks in the Cloudcited by 2
- 2023zkDFL: An efficient and privacy-preserving decentralized federated learning with zero-knowledge proofcited by 1
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