Resilient distributed parameter estimation in heterogeneous time-varying networks

In this paper, we study a lightweight algorithm for distributed parameter estimation in a heterogeneous network in the presence of adversary nodes. All nodes interact under a local broadcast model of communication in a time-varying network comprised of many inexpensive normal nodes, along with several more expensive, reliable nodes. Either the normal or reliable nodes may be tampered with and overtaken by an adversary, thus becoming an adversary node. The reliable nodes have an accurate estimate of their true parameters, whereas the inexpensive normal nodes communicate and take difference measurements with neighbors in the network in order to better estimate their parameters. The normal nodes are unsure, a priori, about which of their neighbors are normal, reliable, or adversary nodes. However, by sharing information on their local estimates with neighbors, we prove that the resilient iterative distributed estimation (RIDE) algorithm, which utilizes redundancy by removing extreme information, is able to drive the local estimates to their true parameters as long as each normal node is able to interact with a sufficient number of reliable nodes often enough and is not directly influenced by too many adversary nodes.

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