Generalized Lagrange Coded Computing: A Flexible Computation-Communication Tradeoff for Resilient, Secure, and Private Computation

TL;DR

This paper introduces Generalized Lagrange Coded Computing (GLCC), a flexible coding scheme that enhances distributed polynomial evaluation by providing resilience, security, and privacy, while improving efficiency and training speed in machine learning applications.

Contribution

GLCC extends Lagrange Coded Computing by offering a more flexible tradeoff between communication and computation, and demonstrates significant speedups in distributed machine learning training.

Findings

GLCC achieves up to 3.9x speedup over LCC in training time.

GLCC provides resilience, security, and privacy simultaneously.

Experimental results on various datasets and models validate effectiveness.

Abstract

We consider the problem of evaluating arbitrary multivariate polynomials over a massive dataset containing multiple inputs, on a distributed computing system with a master node and multiple worker nodes. Generalized Lagrange Coded Computing (GLCC) codes are proposed to simultaneously provide resiliency against stragglers who do not return computation results in time, security against adversarial workers who deliberately modify results for their benefit, and information-theoretic privacy of the dataset amidst possible collusion of workers. GLCC codes are constructed by first partitioning the dataset into multiple groups, then encoding the dataset using carefully designed interpolating polynomials, and sharing multiple encoded data points to each worker, such that interference computation results across groups can be eliminated at the master. Particularly, GLCC codes include the state-of-the-art Lagrange Coded Computing (LCC) codes as a special case, and exhibit a more flexible tradeoff between communication and computation overheads in optimizing system efficiency. Furthermore, we apply GLCC to distributed training of machine learning models, and demonstrate that GLCC codes achieve a speedup of up to $2.5\text{--}3.9\times$ over LCC codes in training time, across experiments for training image classifiers on different datasets, model architectures, and straggler patterns.