Distributed LMS for Consensus-Based In-Network Adaptive Processing

By Ioannis D. Schizas, Gonzalo Mateos, Georgios B. Giannakis. IEEE Transactions on Signal Processing, 2009

390 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mechanics, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/ee764d98241054f128371779fc7dff73cd08de97

How researchers see this paper

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

In Artificial Intelligence

1. Ali H. Sayed
2. Jianshu Chen
3. Zaid J. Towfic
4. Sheng-Yuan Tu
5. Federico S. Cattivelli
6. Haotian Zhang
7. Shreyas Sundaram
8. Xenofon D. Koutsoukos

In Computational Mechanics

1. Ali H. Sayed
2. Jianshu Chen
3. Sheng-Yuan Tu
4. Zaid J. Towfic
5. Federico S. Cattivelli
6. Alexander Olshevsky
7. Angelia Nedić
8. Santiago Zazo

In Computer Networks and Communications

1. Ali H. Sayed
2. Jianshu Chen
3. Sheng-Yuan Tu
4. Zaid J. Towfic
5. Federico S. Cattivelli
6. Shreyas Sundaram
7. Xenofon D. Koutsoukos
8. Alexander Olshevsky

Citations in the corpus

• Excess-Risk of Distributed Stochastic Learners (2013, background): “Recently, there has also been interest in primal-dual approaches for distributed optimization [6], [23]–[26].”
• Adaptive Networks (2014, methodology): “There has also been works on applying the alternating direction method of multipliers (ADMM) [124] to the design of consensus-type algorithms [125], [126].”
• Diffusion Strategies Outperform Consensus Strategies for Distributed Estimation Over Adaptive Networks (2012, methodology): “Such implementations appear in several recent works , including [16]–[19], and are largely motivated by the procedure developed earlier in [15], [23] for the solu ti n of distributed optimization problems.”
• Convergence of a Multi-Agent Projected Stochastic Gradient Algorithm for Non-Convex Optimization (2011, neutral): “In this paper, we investigate a distributed optimization problem which is of practical interest in many multi-agent contexts such as parallel computing [8], statistical estimation [4], [29], [35], [36], robotics [13], or wireless networks [30].”
• Diffusion LMS Strategies for Distributed Estimation (2010, methodology): “The work [14] proposes a distributed LMS algorithm based on consensus techniques that relies on node hierarchy to reduce communications.”
• Adaptation, Learning, and Optimization over Networks (2014, methodology): “The network model used in [217] is not homogeneous and assumes a special structure.”
• Resilient Asymptotic Consensus in Robust Networks (2013, background): “Consensus is fundamental to diverse applications such as data aggregation [9], distributed estimation [10], distributed optimization [11], distributed classification [12], and flocking [13].”