Robust Distributed Diffusion Recursive Least Squares Algorithms With Side Information for Adaptive Networks

By Yi Yu, Haiquan Zhao, R. D. de Lamare, Yuriy V. Zakharov, Lu Lu. 2018

74 citations recorded by the source corpus.

This work develops robust diffusion recursive least-squares algorithms to mitigate the performance degradation often experienced in networks of agents in the presence of impulsive noise. The first algorithm minimizes an exponentially weighted least-squares cost function subject to a time-dependent constraint on the squared norm of the intermediate update at each node. A recursive strategy for computing the constraint is proposed using side information from the neighboring nodes to further improve the robustness. We also analyze the mean-square convergence behavior of the proposed algorithm. The second proposed algorithm is a modification of the first one based on the dichotomous coordinate descent iterations. It has a performance similar to that of the former, however, its complexity is significantly lower especially when input regressors of agents have a shift structure and it is well suited to practical implementation. Simulations show the superiority of the proposed algorithms over previously reported techniques in various impulsive noise scenarios.

Subfields: Computational Mechanics, Statistical and Nonlinear Physics, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Sham M. Kakade
2. Zaïd Harchaoui
3. Krishna Pillutla
4. Zeyuan Allen-Zhu
5. Prateek Jain
6. Jason D. Lee

In Statistical and Nonlinear Physics

1. Sham M. Kakade
2. Aravind Rajeswaran
3. Vikash Kumar

In Signal Processing

1. Sham M. Kakade
2. Zaïd Harchaoui
3. Anima Anandkumar
4. Daniel J. Hsu
5. Sylvain Calinon

Citations in the corpus

• Robust Aggregation for Federated Learning (2019, background): “Robust estimation in networks was considered in [4, 24, 89].”