A resilient convex combination for consensus-based distributed algorithms

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Abstract

Consider a set of vectors in Rn, partitioned into two classes: normal vectors and malicious vectors, for which the number of malicious vectors is bounded but their identities are unknown. The paper provides an efficient way for achieving a resilient convex combination, which is a convex combination of only normal vectors. Compared with existing approaches based on Tverberg points, the proposed method based on the intersection of convex hulls has lower computational complexity. Simulations suggest that the proposed method can be applied to achieve resilience of consensus-based distributed algorithms against Byzantine attacks based only on agents’ locally available information.

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Wang, X., Mou, S., & Sundaram, S. (2019). A resilient convex combination for consensus-based distributed algorithms. Numerical Algebra, Control and Optimization, 9(3), 269–281. https://doi.org/10.3934/naco.2019018

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