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Resilient randomized quantized consensus with delayed information

We investigate resilient consensus in a network of integer-valued agents in the presence of malicious agents. The goal of the healthy, normal agents is to form consensus in their state values, which may be disturbed by the non-normal, malicious agents. In this paper, we study update rules of the agents having asynchrony in the update times and also interagent communications with non-uniform, time varying, and bounded time delays. Using a simple mean subsequence reduced (MSR) algorithm for ignoring the extreme values affecting each normal agent, we solve the resilient quantized consensus problem in the presence of totally bounded adversarial agents. We employ randomization both in quantization and update times. The results are examined through a numerical example.