Ensuring Trustworthy Neural Network Training via Blockchain
Ensuring Trustworthy Neural Network Training via Blockchain
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DOI：
10.1109/cogmi58952.2023.00015
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发表时间：
2023-11
期刊：
影响因子：
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通讯作者：
Edgar Navarro;Kyle J. Standing;Gaby G. Dagher;Tim Andersen
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文献类型：
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作者：
Edgar Navarro;Kyle J. Standing;Gaby G. Dagher;Tim Andersen
随着人工智能的普及，它凸显了依赖受损模型的风险，从而推动了确保训练有素的人工智能模型完整性的日益增长的需求。在本文中，我们提出了一种新颖的基于区块链的系统，旨在验证经过训练的神经网络模型的完整性。该系统通过策略性地重新计算训练过程的间隔来解决模型操纵的风险。此外，区块链网络提供了一个可追踪、不可变、可信的分类账，用于对复杂的训练和验证过程进行编目。我们考虑涉及两个主要实体：“提交者”（提交经过训练的模型）和“验证者”（重新训练所提交模型的不同部分以验证其完整性）。区块链系统的设计通过选择性地针对所有训练间隔的一部分来强调效率。这是通过使用创新的权重分析算法实现的，该算法应用绝对变化方法来识别异常值。我们实施我们的解决方案来证明所提出的区块链系统是稳健的，并且权重分析算法是准确的和可扩展的。
As Artificial Intelligence prevalence grows, it highlights the risk in relying on compromised models, thereby fueling a growing need to ensure the integrity of trained AI models. In this paper, we present a novel blockchain-based system, designed to authenticate the integrity of trained neural network models. The system addresses the risk of manipulation of a model by strategically re-computing intervals of the training process. Further, the blockchain network provides a traceable, immutable, trusted ledger for cataloging the intricate processes of training and validation. We consider two primary entities involved: ‘submitters’, who submit trained models, and ‘verifiers’, who re-train distinct sections of the submitted models to validate their integrity. The design of the blockchain system emphasizes efficiency by selectively targeting a portion of all training intervals. This is made possible through the use of an innovative weight-analysis algorithm, which applies an Absolute Change approach to identify outliers. We implement our solution to demonstrate that the proposed blockchain system is robust, and the weight-analysis algorithm is accurate and scalable.