Secure Byzantine-Robust Machine Learning

• URL: http://arxiv.org/abs/2006.04747v2
• Date: Sun, 18 Oct 2020 22:37:16 GMT
• Title: Secure Byzantine-Robust Machine Learning
• Authors: Lie He and Sai Praneeth Karimireddy and Martin Jaggi
• Abstract summary: 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.
• Score: 61.03711813598128
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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