Consensus in Ad Hoc WSNs With Noisy Links—Part I: Distributed Estimation of Deterministic Signals

By Ioannis D. Schizas, Alejandro Ribeiro, Georgios B. Giannakis. 2008

737 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

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. Wotao Yin
3. Qing Ling
4. Aryan Mokhtari
5. Angelia Nedić
6. Alejandro Ribeiro
7. Wei Shi
8. Kun Yuan

In Computer Networks and Communications

1. Ali H. Sayed
2. Angelia Nedić
3. Wotao Yin
4. Aryan Mokhtari
5. Wei Shi
6. Qing Ling
7. Alexander Olshevsky
8. Kun Yuan

Citations in the corpus

• Robust and Communication-Efficient Collaborative Learning (2019, methodology): “…algorithms (Shi et al., 2015a,b; Mokhtari and Ribeiro, 2016), distributed variants of the alternating direction method of multipliers (ADMM) (Schizas et al., 2008; Boyd et al., 2011; Shi et al., 2014; Chang et al., 2015; Mokhtari et al., 2016), dual averaging (Duchi et al., 2012; Tsianos…”
• On the Convergence of Decentralized Gradient Descent (2013, background): “These properties are important for applications where data are collected by distributed agents, communication to a fusion center is expensive or impossible, and/or agents tend to keep their raw data private; such applications arise in wireless sensor networks [16, 24, 27, 38], multivehicle and multi”
• EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimization (2014, background): “Examples and works include decentralized averaging [7,15,34], learning [9,22,26], estimation [1,2,16,18, 29], sparse optimization [19, 35], and low-rank matrix completion [20] problems.”
• Distributed Alternating Direction Method of Multipliers (2012, background): “In recent works [5], [16] and [10], distributed ADMM algorithms tailored for specific machine learning problems and parameter estimation in wireless sensor networks have been proposed without convergence rate analysis.”
• On the Linear Convergence of the ADMM in Decentralized Consensus Optimization (2013, background): “This formulation arises in averaging [4]–[6], learning [7], [8], and estimation [9]–[13] problems.”
• LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning (2018, background): “Problem (1) naturally arises in a number of areas, such as multiagent optimization (Nedic and Ozdaglar, 2009), distributed signal processing (Giannakis et al., 2016; Schizas et al., 2008), and distributed machine learning (Dean et al.”
• On the O(1=k) convergence of asynchronous distributed alternating Direction Method of Multipliers (2013, methodology): “In this paper, we focus on the more general formulation (1) and propose an asynchronous decentralized algorithm based on the classical Alternating Direction Method of Multipliers (ADMM) (see [2], [3], [4], [5], [13] for convergence analysis of classical ADMM, and also [8], [11] for its applications ”
• On Nonconvex Decentralized Gradient Descent (2016, methodology): “When fi’s are convex, the existing algorithms include the (sub)gradient methods [8], [10], [24], [37], [40], [46], [59], [65], and the primal-dual domain methods such as the decentralized alternating direction method of multipliers (DADMM) [9], [51], [52], DLM [31], and EXTRA [53], [54].”
• Linear Convergence Rate of a Class of Distributed Augmented Lagrangian Algorithms (2013, methodology): “Many known methods to solve (1) fall into the framework of (2)–(3); see, e.g., [9], [4], [5], [6], [7], [8].”
• Decentralized double stochastic averaging gradient (2015, background): “Analogous formulations are also of interest in decentralized control [3], [4] and sensor networks [5]–[7].”
• A Proximal Gradient Algorithm for Decentralized Composite Optimization (2015, methodology): “9 k 0 100 200 300 400 500 R el a ti v e E rr o r 10-10 10-8 10-6 10-4 10-2 100 DSM, step size ,k = 9:5=k1=2 DADMM, penalty coe/cient c = 0:0038 P-EXTRA, step size , = 46 k 0 100 200 300 400 500 S u cc es si ve D i, er en ce 10-10 10-5 100 105 DSM, step size ,k = 9:5=k1=2 DADMM, penalty coe/cient c =”
• Balancing Communication and Computation in Distributed Optimization (2017, methodology): “This class of methods includes augmented Lagrangian methods and alternating direction method of multiplier [3], [35]–[46].”
• Distributed Compressive Spectrum Sensing in Cooperative Multihop Cognitive Networks (2011, methodology): “Different from (8), each CR locally performs the following consensus optimization [13], [17]:”
• Federated Learning With Cooperating Devices: A Consensus Approach for Massive IoT Networks (2019, methodology): “The proposed FL approaches exploit both adaptive diffusion [31] and consensus tools [23], [27] to optimally leverage the (possibly large) population of federated devices that cooperate for the distributed estimation of the global model W, while retaining the trained data.”
• Distributed Linearized Alternating Direction Method of Multipliers for Composite Convex Consensus Optimization (2015, background): “This computational setting, i.e., decentralized consensus optimization, appears as a generic model for various applications in signal processing, e.g., [2]–[6], machine learning, e.g., [7]–[9] and statistical inference, e.g., [10], [11].”
• EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimization (2014, background): “[9, 22, 26], estimation [1, 2, 16, 18, 29], sparse optimization [19, 35], and low-rank matrix completion [20] problems.”