提出低通信量抗攻击分布式估计方法，提升网络鲁棒性。
Low-Communication Resilient Distributed Estimation Algorithm Based on Memory Mechanism
- 基于声誉的节点选择与记忆数据训练的加权支持向量描述模型
- 在有限通信下实现高精度估计，有效抵御恶意节点干扰
- 适合资源受限且需防攻击的分布式系统应用
在多任务对抗网络中，分布式算法对未知参数的准确估计常受恶意节点或链路影响。本文提出一种低通信量、抗干扰的分布式估计算法。首先引入基于声誉的节点选择策略，使节点仅与更可靠的邻居通信；随后，采用加权支持向量数据描述（W-SVDD）模型对记忆数据进行训练，以识别可信中间估计结果，增强估计过程对恶意节点或链路的鲁棒性；此外，设计事件触发机制以减少对W-SVDD模型的无效更新，并基于假设推导出合适阈值；最后通过仿真验证，该算法在通信成本更低的前提下，性能优于其他算法。
原文摘要 · Abstract (English)
In multi-task adversarial networks, the accurate estimation of unknown parameters in a distributed algorithm is hindered by attacked nodes or links. To tackle this challenge, this brief proposes a low-communication resilient distributed estimation algorithm. First, a node selection strategy based on reputation is introduced that allows nodes to communicate with more reliable subset of neighbors. Subsequently, to discern trustworthy intermediate estimates, the Weighted Support Vector Data Description (W-SVDD) model is employed to train the memory data. This trained model contributes to reinforce the resilience of the distributed estimation process against the impact of attacked nodes or links. Additionally, an event-triggered mechanism is introduced to minimize ineffective updates to the W-SVDD model, and a suitable threshold is derived based on assumptions. The convergence of the algorithm is analyzed. Finally, simulation results demonstrate that the proposed algorithm achieves superior performance with less communication cost compared to other algorithms.
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