Data privacy and sharing has always been a critical issue when trying to build complex deep learning-based systems to model data. Facilitation of a decentralized approach that could take benefit from data across multiple nodes while not needing to merge their data contents physically has been an area of active research. In this paper, we present a solution to benefit from a distributed data setup in the case of training deep learning architectures by making use of a smart contract system. Specifically, we propose a mechanism that aggregates together the intermediate representations obtained from local ANN models over a blockchain. Training of local models takes place on their respective data. The intermediate representations derived from them, when combined and trained together on the host node, helps to get a more accurate system. While federated learning primarily deals with the same features of data where the number of samples being distributed on multiple nodes, here we are dealing with the same number of samples but with their features being distributed on multiple nodes. We consider the task of bank loan prediction wherein the personal details of an individual and their bank-specific details may not be available at the same place. Our aggregation mechanism helps to train a model on such existing distributed data without having to share and concatenate together the actual data values. The obtained performance, which is better than that of individual nodes, and is at par with that of a centralized data setup makes a strong case for extending our technique across other architectures and tasks. The solution finds its application in organizations that want to train deep learning models on vertically partitioned data.

中文翻译：

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使用智能合约系统进行银行贷款预测的深度学习模型的分散聚合机制

尝试构建基于深度学习的复杂系统来对数据进行建模时，数据隐私和共享一直是关键问题。分散化方法的开发一直是积极研究的领域，该方法可以从多个节点的数据中受益，而无需物理上合并其数据内容。在本文中，我们提出了一种在利用智能合约系统训练深度学习架构的情况下从分布式数据设置中受益的解决方案。具体来说，我们提出了一种机制，该机制将通过区块链从本地ANN模型获得的中间表示汇总在一起。本地模型的训练在它们各自的数据上进行。从它们派生的中间表示形式在主机节点上进行组合和训练时，有助于获得更准确的系统。尽管联合学习主要处理样本数量分布在多个节点上的相同数据特征，但是在这里，我们处理样本数量相同但样本分布在多个节点上的数据。我们考虑了银行贷款预测的任务，其中个人的个人详细信息及其银行特定的详细信息可能无法在同一位置使用。我们的汇总机制有助于在此类现有分布式数据上训练模型，而不必共享和连接实际数据值。获得的性能优于单个节点，并且与集中式数据设置相当，这为将我们的技术扩展到其他体系结构和任务提供了有力的理由。