Brief Announcement: Byzantine-Tolerant Machine Learning

被引：2

作者：

Blanchard, Peva ^{[1
]}

El Mhamdi, El Mahdi ^{[1
]}

Guerraoui, Rachid ^{[1
]}

Stainer, Julien ^{[1
]}

机构：

[1] Swiss Fed Inst Technol, Lausanne, Switzerland

来源：

PROCEEDINGS OF THE ACM SYMPOSIUM ON PRINCIPLES OF DISTRIBUTED COMPUTING (PODC'17) | 2017年

基金：

瑞士国家科学基金会;

关键词：

Distributed Stochastic Gradient Descent; Adversarial Machine Learning;

D O I：

10.1145/3087801.3087861

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

We report on Krum, the first provably Byzantine-tolerant aggregation rule for distributed Stochastic Gradient Descent (SGD). Krum guarantees the convergence of SGD even in a distributed setting where (asymptotically) up to half of the workers can be malicious adversaries trying to attack the learning system.

引用

页码：455 / 457

页数：3

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