简短公告:放松拜占庭矢量共识

Zhuolun Xiang, N. Vaidya
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引用次数: 4

摘要

拜占庭向量共识要求无缺陷过程在无缺陷过程输入的凸包中就决策(或输出)达成一致。最近的研究表明,当输入为d维实数向量时,n≥max{(3f+1,(d+1)f+1)}是同步系统的紧界,而n≥(d+2)f+1是异步系统的近似一致性的紧界。由于下界依赖于向量维数d,当向量维数较大时,所需的处理数也就较大。为了减小n的下界,我们提出了拜占庭向量共识的放宽版本:k-放宽拜占庭向量共识和(δ,p)-放宽拜占庭向量共识。k松弛一致性只要求在k维的每个子集上输入的投影的一致性。(δ,p)-松弛共识要求输出在非故障输入凸包的距离δ内,其中距离是使用lp范数定义的。输入依赖的δ允许与非故障凸包的距离依赖于非故障输入之间的最大距离。我们证明了对于k-松弛一致性和常数δ≥0的(δ,p)-松弛一致性,n上的界与原向量一致性问题的界相同。另一方面,当δ依赖于输入时,我们证明当d≥3时n的界较小。投入依赖δ在实践中可能很有趣——本质上,投入依赖δ随投入的扩散而变化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Brief Announcement: Relaxed Byzantine Vector Consensus
Byzantine vector consensus requires that non-faulty processes reach agreement on a decision (or output) that is in the convex hull of the inputs at the non-faulty processes. Recent work has shown that, for n processes with up to f Byzantine failures, when the inputs are d-dimensional vectors of reals, n ≥ max{(3f+1,(d+1)f+1)} is the tight bound for synchronous systems, and n≥(d+2)f+1 is tight for approximate consensus in asynchronous systems. Due to the dependence of the lower bound on vector dimension d, the number of processes necessary becomes large when the vector dimension is large. With the hope of reducing the lower bound on n, we propose relaxed versions of Byzantine vector consensus: k-relaxed Byzantine vector consensus and (δ,p)-relaxed Byzantine vector consensus. k-relaxed consensus only requires consensus for projections of inputs on every subset of k dimensions. (δ,p)-relaxed consensus requires that the output be within distance δ of the convex hull of the non-faulty inputs, where distance is defined using the Lp-norm. An input-dependent δ allows the distance from the non-faulty convex hull to be dependent on the maximum distance between the non-faulty inputs. We show that for k-relaxed consensus and (δ,p)-relaxed consensus with constant δ≥0, the bound on n is identical to the bound stated above for the original vector consensus problem. On the other hand, when δ depends on the inputs, we show that the bound on n is smaller when d ≥ 3. Input-dependent δ may be of interest in practice -- in essence, input-dependent δ scales with the spread of the inputs.
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