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Distributed algorithms provide flexibility over centralized algorithms forresource allocation problems, e.g., cyber-physical systems. However, thedistributed nature of these algorithms often makes the systems susceptible toman-in-the-middle attacks, especially when messages are transmitted betweenprice-taking agents and a central coordinator. We propose a resilient strategyfor distributed algorithms under the framework of primal-dual distributedoptimization. We formulate a robust optimization model that accounts forByzantine attacks on the communication channels between agents and coordinator.We propose a resilient primal-dual algorithm using state-of-the-art robuststatistics methods. The proposed algorithm is shown to converge to aneighborhood of the robust optimization model, where the neighborhood's radiusis proportional to the fraction of attacked channels.

Resilient Distributed Optimization Algorithms for Resource Allocation