Secure Byzantine-Robust Machine Learning

Authors: Lie He, Sai Praneeth Karimireddy, Martin Jaggi
Published: 2020-06-08 | Updated: 2020-10-18

Source: https://arxiv.org/abs/2006.04747

PDF: https://arxiv.org/pdf/2006.04747

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連合学習 プライバシー評価 MPCアルゴリズム

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Abstract

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.