Split-n-Chain: Privacy-Preserving Multi-Node Split Learning with Blockchain-Based Auditability

• URL: http://arxiv.org/abs/2503.07570v2
• Date: Tue, 15 Apr 2025 06:22:26 GMT
• Title: Split-n-Chain: Privacy-Preserving Multi-Node Split Learning with Blockchain-Based Auditability
• Authors: Mukesh Sahani, Binanda Sengupta,
• Abstract summary: Split-n-Chain is a variant of split learning where the layers of the network are split among several distributed nodes.<n>We show that Split-n-Chain is efficient, in terms of time required to execute different phases, and the training loss trend is similar to that for the same neural network when implemented in a monolithic fashion.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning, when integrated with a large amount of training data, has the potential to outperform machine learning in terms of high accuracy. Recently, privacy-preserving deep learning has drawn significant attention of the research community. Different privacy notions in deep learning include privacy of data provided by data-owners and privacy of parameters and/or hyperparameters of the underlying neural network. Federated learning is a popular privacy-preserving execution environment where data-owners participate in learning the parameters collectively without leaking their respective data to other participants. However, federated learning suffers from certain security/privacy issues. In this paper, we propose Split-n-Chain, a variant of split learning where the layers of the network are split among several distributed nodes. Split-n-Chain achieves several privacy properties: data-owners need not share their training data with other nodes, and no nodes have access to the parameters and hyperparameters of the neural network (except that of the respective layers they hold). Moreover, Split-n-Chain uses blockchain to audit the computation done by different nodes. Our experimental results show that: Split-n-Chain is efficient, in terms of time required to execute different phases, and the training loss trend is similar to that for the same neural network when implemented in a monolithic fashion.

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