List-Decodable Coded Computing: Breaking the Adversarial Toleration Barrier

TL;DR

This paper introduces list-decoding techniques for coded computing, enabling the system to tolerate more adversarial workers than previously possible by leveraging side information and novel algorithms.

Contribution

It proposes a new approach using list-decoding and side information to surpass existing adversarial tolerance limits in coded computing.

Findings

FLCC outperforms LCC in adversarial tolerance

Threshold improved by a factor of two asymptotically

Effective use of side information for decoding

Abstract

We consider the problem of coded computing, where a computational task is performed in a distributed fashion in the presence of adversarial workers. We propose techniques to break the adversarial toleration threshold barrier previously known in coded computing. More specifically, we leverage list-decoding techniques for folded Reed-Solomon codes and propose novel algorithms to recover the correct codeword using side information. In the coded computing setting, we show how the master node can perform certain carefully designed extra computations to obtain the side information. The workload of computing this side information is negligible compared to the computations done by each worker. This side information is then utilized to prune the output of the list decoder and uniquely recover the true outcome. We further propose folded Lagrange coded computing (FLCC) to incorporate the developed techniques into a specific coded computing setting. Our results show that FLCC outperforms LCC by breaking the barrier on the number of adversaries that can be tolerated. In particular, the corresponding threshold in FLCC is improved by a factor of two asymptotically compared to that of LCC.