论文标题
通过体重学习及其在易于故障时钟同步中使用的弹性共识
Resilient Consensus via Weight Learning and Its Application in Fault-Tolerant Clock Synchronization
论文作者
论文摘要
本文解决了存在错误的节点的分布式共识问题。引入了一种新型的体重学习算法，以使网络连接性和一系列历史记录都不需要实现弹性共识。关键的想法是动态更新邻居中从其信誉度量中学到的相互作用权重。基本上，我们定义了一个与邻居距离成反比的奖励功能，然后根据目前步骤和上一个信誉的奖励调整信誉。以这种方式，相互作用的权重在每个步骤中都会更新，从而集成了历史信息并从故障节点中降低影响。本文考虑了固定拓扑和随机拓扑。此外，我们将这种新颖的方法应用于时钟同步问题。通过分别通过相应的重量学习算法更新逻辑时钟偏斜和偏移，无论节点错误如何，最终都可以实现逻辑时钟同步。提供仿真来说明该战略的有效性。
This paper addresses the distributed consensus problem in the presence of faulty nodes. A novel weight learning algorithm is introduced such that neither network connectivity nor a sequence of history records is required to achieve resilient consensus. The critical idea is to dynamically update the interaction weights among neighbors learnt from their credibility measurement. Basically, we define a reward function that is inversely proportional to the distance to its neighbor, and then adjust the credibility based on the reward derived at the present step and the previous credibility. In such a way, the interaction weights are updated at every step, which integrates the historic information and degrades the influences from faulty nodes. Both fixed and stochastic topologies are considered in this paper. Furthermore, we apply this novel approach in clock synchronization problem. By updating the logical clock skew and offset via the corresponding weight learning algorithms, respectively, the logical clock synchronization is eventually achieved regardless of faulty nodes. Simulations are provided to illustrate the effectiveness of the strategy.
