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A resilient convex combination for consensus-based distributed algorithms

This work is supported by a seed grant from Purdue Center for Resilient Infrastructures, Systems, and Processes (CRISP). The research of Xuan Wang and Shaoshuai Mou is supported by fundings from Northrop Grumman Corporation. The research of Shreyas Sundaram is supported by the National Science Foundation CAREER award 1653648

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  • Consider a set of vectors in $ \mathbb{R}^n $, 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.

    Mathematics Subject Classification: Primary: 68Q85; Secondary: 11D04.


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  • Figure 1.  Finding Tverberg point $ \mathcal{T} $ (yellow) and $ \mathcal{R} $ (red) in a 2-D space, with $ \bar{\mathcal{A}} = \{1\} $

    Figure 2.  A network of 11 agents with malicious agents marked in red

    Figure 3.  Simulations of normal agents under the consensus update (20) without malicious agents (blank line) and with malicious agents $ 10 $ and $ 11 $ (red line)

    Figure 4.  Consensus is reached by introducing $ u_i(t) $ as Tverberg points (indicated by the dashed line) or as the resilient convex combination (17) (indicated by the solid line)

    Figure 5.  Simulations by using the resilient convex combination $ u_i(t) $ of (17) into (22)

    Figure 6.  Simulation results under the update (23) with no malicious agents (indicated by the black line) or with malicious agents (indicated by the red line)

    Figure 7.  Simulations by using the resilient convex combination $ u_i(t) $ of (17) in (23)

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