Linear Network Coding for Robust Function Computation and Its Applications in Distributed Computing

TL;DR

This paper advances linear network coding techniques for robust function computation, especially sum and identity functions, and applies them to distributed computing with improved fault tolerance and security.

Contribution

It introduces a minimum distance decoder for linear network codes and designs a distributed gradient coding scheme with enhanced robustness and efficiency.

Findings

Codes attain the Singleton-like bound for sum and identity functions.

The distributed scheme balances straggler tolerance, cost, and security.

The approach defends against Byzantine attacks.

Abstract

We investigate linear network coding in the context of robust function computation, where a sink node is tasked with computing a target function of messages generated at multiple source nodes. In a previous work, a new distance measure was introduced to evaluate the error tolerance of a linear network code for function computation, along with a Singleton-like bound for this distance. In this paper, we first present a minimum distance decoder for these linear network codes. We then focus on the sum function and the identity function, showing that in any directed acyclic network there are two classes of linear network codes for these target functions, respectively, that attain the Singleton-like bound. Additionally, we explore the application of these codes in distributed computing and design a distributed gradient coding scheme in a heterogeneous setting, optimizing the trade-off between straggler tolerance, computation cost, and communication cost. This scheme can also defend against Byzantine attacks.