Distributed Computing with Channel Noise

TL;DR

This paper presents a method for reliably simulating distributed protocols over noisy channels with malicious adversaries, achieving near-optimal communication efficiency without prior knowledge of message sizes or adversary strength.

Contribution

It introduces a robust simulation technique that adapts to unknown message sizes and adversarial noise, ensuring low failure probability and near-optimal communication overhead.

Findings

Achieves robust protocol simulation with $ ilde{O}(L + T)$ bits.

Provides an adaptive algorithm that adjusts to message size $eta$.

Ensures failure probability at most $ ext{delta}$.

Abstract

A group of $n$ users want to run a distributed protocol $\pi$ over a network where communication occurs via private point-to-point channels. Unfortunately, an adversary, who knows $\pi$, is able to maliciously flip bits on the channels. Can we efficiently simulate $\pi$ in the presence of such an adversary? We show that this is possible, even when $L$, the number of bits sent in $\pi$, and $T$, the number of bits flipped by the adversary are not known in advance. In particular, we show how to create a robust version of $\pi$ that 1) fails with probability at most $\delta$, for any $\delta>0$; and 2) sends $\tilde{O}(L + T)$ bits, where the $\tilde{O}$ notation hides a $\log (nL/ \delta)$ term multiplying $L$. Additionally, we show how to improve this result when the average message size $\alpha$ is not constant. In particular, we give an algorithm that sends $O( L (1 + (1/\alpha) \log (n L/\delta) + T)$ bits. This algorithm is adaptive in that it does not require a priori knowledge of $\alpha$. We note that if $\alpha$ is $\Omega\left( \log (n L/\delta) \right)$, then this improved algorithm sends only $O(L+T)$ bits, and is therefore within a constant factor of optimal.