Resilient consensus‐based distributed optimization under deception attacks

Abstract

Summary This article considers distributed optimization problems of complex cyber‐physical networks, whose goal is to minimize a global function consisting of a sum of local functions possessed by each node, when the communication network is suffering L ‐local deception attacks. After showing that merely 1‐local deception attacks can arbitrarily affect the outcome of any distributed optimization algorithms without being detected, we propose a resilient consensus‐based distributed optimization algorithm, where the estimation for the optimizer of each node is updated according to its subgradient and its partial neighbors' estimation. Then, we provide the conditions for the proposed algorithm to ensure that all the nodes can make an agreement and converge to the convex hull of the local optimizer of their functions in the presence of L ‐local deception attacks. Finally, some simulation examples are presented to demonstrate the effectiveness of the proposed algorithm.