We consider the problem of evaluating arbitrary multivariate polynomials over several massive datasets in a distributed computing system with a single master node and multiple worker nodes. We focus on the general case when each multivariate polynomial is evaluated over its dataset and propose a generalization of the Lagrange Coded Computing framework (Yu et al. 2019) to provide robustness against stragglers who do not respond in time, adversarial workers who respond with wrong computation and information-theoretic security of dataset against colluding workers. Our scheme introduces a small computation overhead which results in a reduction in download cost and also offers comparable resistance to stragglers over existing solutions.
翻译:我们考虑在分布式计算机系统中对多个大型数据集的任意多变量多元值进行评估的问题,这个系统有一个单一的主节点和多个工人节点,我们侧重于每个多变量多元值对其数据集进行评估的一般情况,并提议对拉格朗代碼计算框架(Yu等人,2019年)进行概括,以便对不及时回应的挤压者、以错误计算和对串通工人的数据数据集信息理论安全做出回应的敌对工人提供稳健性。 我们的计划引入了一个小的计算间接成本,导致下载成本的降低,同时也为对现有解决方案的挤压者提供了类似的抗力。