Stabilization, Safety, and Security of Distributed Systems

Consensus algorithms allow a set of nodes to reach an agreement on a quantity of interest. For instance, a consensus algorithm may be used to allow a network of sensors to determine the average value of samples collected by the different sensors. Similarly, a consensus algorithm can also be used by the nodes to synchronize their clocks. Research on consensus algorithms has a long history, with contributions from different research communities, including distributed computing, control systems, and social science. In this talk, we will discuss two resilient consensus algorithms that can perform correctly despite the following two types of adversities: (i) In wireless networks, transmissions are subject to transmission errors, resulting in packet losses. We will discuss how “average consensus” can be achieved over such lossy links, without explicitly making the links reliable, for instance, via retransmissions. (ii) In a distributed setting, some of the nodes in the network may fail or may be compromised. We will discuss a consensus algorithm that can tolerate “Byzantine” failures in partially connected networks. Low-Congestion Distributed Algorithms

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