Reputation-Based Byzantine-Resilient Vector Consensus without Prespecified Fault Bounds
Abstract
This paper proposes an adaptive reputation-based algorithm for Byzantine-resilient vector consensus in distributed multi-agent systems. The proposed algorithm does not require a prespecified bound on the number of Byzantine neighbors as an algorithmic input. Instead, each agent evaluates neighboring behavior from observed state discrepancies and updates reputation weights using accumulated behavioral evidence. The reputation mechanism adaptively redistributes communication weights and can assign exactly zero weight to sufficiently inconsistent neighbors. These reputation weights are incorporated into the local consensus updates to suppress adversarial influence while preserving reliable neighbor interactions. The resulting algorithm couples reputation adaptation with consensus dynamics while supporting high-dimensional vector states with low per-agent computational complexity. Extensive experiments on distributed systems against classical resilient consensus methods demonstrate improved Byzantine detection accuracy and more reliable consensus performance across different attack scenarios and state dimensions.
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BibTeX
@misc{huang2026reputationbased,
title = {{Reputation-Based Byzantine-Resilient Vector Consensus without Prespecified Fault Bounds}},
author = {Rui Huang and Changxin Liu and Yang Shi},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2605.11357},
eprint = {2605.11357},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2605.11357},
}
RIS
TY - UNPB
TI - Reputation-Based Byzantine-Resilient Vector Consensus without Prespecified Fault Bounds
AU - Rui Huang
AU - Changxin Liu
AU - Yang Shi
PY - 2026
DA - 2026/09/17
PB - arXiv
DO - 10.48550/arxiv.2605.11357
UR - https://arxiv.org/abs/2605.11357
AB - This paper proposes an adaptive reputation-based algorithm for Byzantine-resilient vector consensus in distributed multi-agent systems. The proposed algorithm does not require a prespecified bound on the number of Byzantine neighbors as an algorithmic input. Instead, each agent evaluates neighboring behavior from observed state discrepancies and updates reputation weights using accumulated behavioral evidence. The reputation mechanism adaptively redistributes communication weights and can assign exactly zero weight to sufficiently inconsistent neighbors. These reputation weights are incorporated into the local consensus updates to suppress adversarial influence while preserving reliable neighbor interactions. The resulting algorithm couples reputation adaptation with consensus dynamics while supporting high-dimensional vector states with low per-agent computational complexity. Extensive experiments on distributed systems against classical resilient consensus methods demonstrate improved Byzantine detection accuracy and more reliable consensus performance across different attack scenarios and state dimensions.
ER -