With the development of distributed algorithms, many researchers are committed to the goal of maintaining the long-term stability of the network by reducing the communication cost. However, many algorithms that lessen communication costs often result in a significant decrease in estimation accuracy. In order to reduce the communication cost with less performance degradation, the distributed neighbor-partial diffusion least-mean-square algorithm (NPDLMS) is proposed in this paper. Besides, considering the data redundancy in the network, we offer the distributed data selection NPDLMS algorithm, which further improves the estimation accuracy and reduces the communication cost. In the performance analysis, the stability and the communication cost of the algorithms are given.

中文翻译：

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具有邻域偏扩散的分布式最小均方算法的通信减少算法

随着分布式算法的发展，许多研究人员致力于通过降低通信成本来维持网络长期稳定的目标。然而，许多降低通信成本的算法通常会导致估计精度的显着下降。为了在降低性能下降的情况下降低通信成本，本文提出了分布式邻域部分扩散最小均方算法（NPDLMS）。此外，考虑到网络中的数据冗余，我们提供了分布式数据选择NPDLMS算法，进一步提高了估计精度并降低了通信成本。在性能分析中，给出了算法的稳定性和通信开销。