论文标题
安全拜占庭式机器学习
Secure Byzantine-Robust Machine Learning
论文作者
论文摘要
越来越多的机器学习系统被部署到边缘服务器和设备（例如手机），并以协作方式进行培训。这种分布式/联合/分散的培训引起了人们对程序的鲁棒性，隐私和安全性的许多担忧。尽管在以鲁棒性，隐私或安全性来解决方案方面已经完成了广泛的工作，但很少研究它们的组合。在本文中，我们提出了一个安全的两效协议，该协议既提供输入隐私和拜占庭式持久性。此外，该协议是沟通效率，容忍性的，并享有当地的差异隐私。
Increasingly machine learning systems are being deployed to edge servers and devices (e.g. mobile phones) and trained in a collaborative manner. Such distributed/federated/decentralized training raises a number of concerns about the robustness, privacy, and security of the procedure. While extensive work has been done in tackling with robustness, privacy, or security individually, their combination has rarely been studied. In this paper, we propose a secure two-server protocol that offers both input privacy and Byzantine-robustness. In addition, this protocol is communication-efficient, fault-tolerant and enjoys local differential privacy.
