Heterogeneous big data poses many challenges in machine learning. Its enormous scale, high dimensionality, and inherent uncertainty make almost every aspect of machine learning difficult, from providing enough processing power to maintaining model accuracy to protecting privacy. However, perhaps the most imposing problem is that big data is often interspersed with sensitive personal data. Hence, we propose a privacy-preserving hierarchical fuzzy neural network to address these technical challenges while also alleviating privacy concerns. The network is trained with a two-stage optimization algorithm, and the parameters at low levels of the hierarchy are learned with a scheme based on the well-known alternating direction method of multipliers, which does not reveal local data to other agents. Coordination at high levels of the hierarchy is handled by the alternating optimization method, which converges very quickly. The entire training procedure is scalable, fast, and does not suffer from gradient vanishing problems like the methods based on backpropagation. Comprehensive simulations conducted on both regression and classification tasks demonstrate the effectiveness of the proposed model. Our code is available online.1

中文翻译：

---

基于异构大数据隐私保护的分层模糊神经网络

异构大数据给机器学习带来了许多挑战。它的巨大规模、高维度和固有的不确定性使机器学习的几乎每个方面都变得困难，从提供足够的处理能力到保持模型准确性到保护隐私。然而，也许最严重的问题是大数据中经常穿插着敏感的个人数据。因此，我们提出了一种保护隐私的分层模糊神经网络来解决这些技术挑战，同时减轻隐私问题。该网络采用两阶段优化算法进行训练，层次结构低层的参数采用基于众所周知的乘法器交替方向方法的方案学习，该方案不会向其他代理透露本地数据。层次结构高层的协调由交替优化方法处理，该方法收敛速度非常快。整个训练过程是可扩展的、快速的，并且不会像基于反向传播的方法那样存在梯度消失问题。在回归和分类任务上进行的综合模拟证明了所提出模型的有效性。我们的代码可在线获取。1