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 (PP-HFNN) 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 back-propagation. Comprehensive simulations conducted on both regression and classification tasks demonstrate the effectiveness of the proposed model.

翻译：在机器学习中,高层次的巨型数据带来了许多挑战。 其规模巨大、多维性和内在的不确定性几乎使机器学习的每一个方面都困难重重,从提供足够的处理能力到保持模型准确性以保护隐私。然而,也许最棘手的问题是,大数据往往与敏感的个人数据混杂在一起。因此,我们提议建立一个保护隐私的分层模糊神经网络(PP-HFNN)来应对这些技术挑战,同时减轻对隐私的担忧。这个网络接受两阶段优化算法的培训,低层次的参数则通过一个以众所周知的乘数交替方向方法为基础的计划来学习,该方法不会向其他代理方披露当地数据。 高层的协调是由交替优化方法处理的,该方法非常快速地汇合。整个培训程序是可伸缩的,快速的,不会因梯度消失的问题如基于反调的方法而受到影响。在回归和分类任务上进行的全面模拟显示了拟议模型的有效性。