# Verifiable Split Learning via zk-SNARKs

arXiv: https://arxiv.org/abs/2511.01356 · HTML: https://mlsanity.com/paper/2511.01356/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 19 references that are in the map.
- First posted on arXiv 3 Nov 2025.

## Builds on (most cited first, 12 of 12)

- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [Distributed learning of deep neural network over multiple agents](https://mlsanity.com/paper/1810.06060/) (2018)
- [Unleashing the Tiger: Inference Attacks on Split Learning](https://mlsanity.com/paper/2012.02670/) (2020)
- [martFL: Enabling Utility-Driven Data Marketplace with a Robust and Verifiable Federated Learning Architecture](https://mlsanity.com/paper/2309.01098/) (2023)
- [Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning](https://mlsanity.com/paper/2403.19178/) (2024)
- [A Review of zk-SNARKs](https://mlsanity.com/paper/2202.06877/) (2022)
- [Love or Hate? Share or Split? Privacy-Preserving Training Using Split Learning and Homomorphic Encryption](https://mlsanity.com/paper/2309.10517/) (2023)
- [Enhancing Accuracy-Privacy Trade-off in Differentially Private Split Learning](https://mlsanity.com/paper/2310.14434/) (2023)
- [Zero-Knowledge Proof Frameworks: A Systematic Survey](https://mlsanity.com/paper/2502.07063/) (2025)
- [VerifBFL: Leveraging zk-SNARKs for A Verifiable Blockchained Federated Learning](https://mlsanity.com/paper/2501.04319/) (2025)
- [Towards Privacy-Preserving Split Learning: Destabilizing Adversarial Inference and Reconstruction Attacks in the Cloud](https://mlsanity.com/paper/2502.20629/) (2025)
- [zkDFL: An efficient and privacy-preserving decentralized federated learning with zero-knowledge proof](https://mlsanity.com/paper/2312.04579/) (2023)

## 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.