Byzantine Multi-Agent Optimization: Part I

TL;DR

This paper addresses Byzantine fault-tolerance in distributed convex optimization, proposing algorithms that ensure a significant subset of non-faulty agents influence the solution despite arbitrary malicious behavior.

Contribution

It introduces a new formulation of Byzantine-resilient distributed optimization and provides algorithms guaranteeing a large fraction of non-faulty agents' influence.

Findings

Maximum of |N|-f nonzero weights achievable

Algorithms ensure at least |N|-f agents have weights bounded away from zero

A low-complexity suboptimal algorithm guarantees influence of at least half of the agents

Abstract

We study Byzantine fault-tolerant distributed optimization of a sum of convex (cost) functions with real-valued scalar input/ouput. In particular, the goal is to optimize a global cost function $\frac{1}{|\mathcal{N}|}\sum_{i\in \mathcal{N}} h_i(x)$, where $\mathcal{N}$ is the set of non-faulty agents, and $h_i(x)$ is agent $i$'s local cost function, which is initially known only to agent $i$. In general, when some of the agents may be Byzantine faulty, the above goal is unachievable, because the identity of the faulty agents is not necessarily known to the non-faulty agents, and the faulty agents may behave arbitrarily. Since the above global cost function cannot be optimized exactly in presence of Byzantine agents, we define a weaker version of the problem. The goal for the weaker problem is to generate an output that is an optimum of a function formed as a convex combination of local cost functions of the non-faulty agents. More precisely, for some choice of weights $\alpha_i$ for $i\in \mathcal{N}$ such that $\alpha_i\geq 0$ and $\sum_{i\in \mathcal{N}}\alpha_i=1$, the output must be an optimum of the cost function $\sum_{i\in \mathcal{N}} \alpha_ih_i(x)$. Ideally, we would like $\alpha_i=\frac{1}{|\mathcal{N}|}$ for all $i\in \mathcal{N}$ -- however, this cannot be guaranteed due to the presence of faulty agents. In fact, we show that the maximum achievable number of nonzero weights ($\alpha_i$'s) is $|\mathcal{N}|-f$, where $f$ is the upper bound on the number of Byzantine agents. In addition, we present algorithms that ensure that at least $|\mathcal{N}|-f$ agents have weights that are bounded away from 0. We also propose a low-complexity suboptimal algorithm, which ensures that at least $\lceil \frac{n}{2}\rceil-\phi$ agents have weights that are bounded away from 0, where $n$ is the total number of agents, and $\phi$ ($\phi\le f$) is the actual number of Byzantine agents.