Privacy, security and data governance constraints rule out a brute force process in the integration of cross-silo data, which inherits the development of the Internet of Things. Federated learning is proposed to ensure that all parties can collaboratively complete the training task while the data is not out of the local. Vertical federated learning is a specialization of federated learning for distributed features. To preserve privacy, homomorphic encryption is applied to enable encrypted operations without decryption. Nevertheless, together with a robust security guarantee, homomorphic encryption brings extra communication and computation overhead. In this paper, we analyze the current bottlenecks of vertical federated learning under homomorphic encryption comprehensively and numerically. We propose a straggler-resilient and computation-efficient accelerating system that reduces the communication overhead in heterogeneous scenarios by 65.26% at most and reduces the computation overhead caused by homomorphic encryption by 40.66% at most. Our system can improve the robustness and efficiency of the current vertical federated learning framework without loss of security.

翻译：数据隐私、安全性和数据治理约束排除了跨孤岛数据集成中的暴力处理方式，这源于物联网的发展。联邦学习被提出以确保所有参与方能够在数据不出本地的情况下协作完成训练任务。纵向联邦学习是联邦学习针对分布式特征的一种专门化方法。为保护隐私，同态加密被用于在不解密的情况下实现加密操作。然而，同态加密在提供强大安全保障的同时，也带来了额外的通信和计算开销。本文全面且量化地分析了同态加密下纵向联邦学习的当前瓶颈。我们提出了一种抗掉队者且计算高效的加速系统，在异构场景下最多可减少65.26%的通信开销，并最多可减少由同态加密引起的40.66%的计算开销。我们的系统能在不损失安全性的前提下，提升当前纵向联邦学习框架的鲁棒性和效率。