Quantized Consensus

By Akshay Kashyap, Tamer Ba¸sar, Rayadurgam Srikant, Ian R. Petersen. 2006

753 citations recorded by the source corpus.

Subfields: Computer Networks and Communications

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Citations in the corpus

Robust and Communication-Efficient Collaborative Learning (2019, methodology): “In the decentralized setting, quantization methods were proposed in different convex optimization contexts with nonvanishing errors (Yuksel and Basar, 2003; Rabbat and Nowak, 2005; Kashyap et al., 2006; El Chamie et al., 2016; Aysal et al., 2007; Nedic et al., 2008).”
D2: Decentralized Training over Decentralized Data (2018, background): “In contrast, decentralized algorithms can work on any connected network and only rely on the information exchange between neighbor workers [Kashyap et al., 2007, Lavaei and Murray, 2012, Nedic et al., 2009].”
An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination (2012, background): “In most studies of the asynchronous consensus problem for networked systems, due to the intrinsic technical difficulties, usually only single-integrator kinematics (1) and double-integrator dynamics are considered.”
Communication Compression for Decentralized Training (2018, methodology): “Other compression methods can be found in Kashyap et al. [2007], Lavaei and Murray [2012], Nedic et al. [2009].”
Gossip consensus algorithms via quantized communication (2009, methodology): “2 is a variation of Lemma 3 and Theorem 1 in [9].”
Distributed subgradient methods and quantization effects (2008, background): “Closely related is also the work in [10] and [7], [6], which study the effects of quantization on the performance of averaging algorithms.”
An Exact Quantized Decentralized Gradient Descent Algorithm (2018, background): “Furthermore, consensus distributed averaging algorithms are studied under discretized message passing [26].”
Consensus Conditions of Multi-Agent Systems With Time-Varying Topologies and Stochastic Communication Noises (2010, background): “…the sending, transmission and receiving of information, such as thermal noise, channel fading, quantization effect during encoding and decoding [18], etc. Consensus of dynamic networks with stochastic communication noises is a common problem in distributed systems [19], and has attracted the…”
Design and analysis of distributed averaging with quantized communication (2014, background): “The probabilistic algorithm in [19] causes all n agents reach quantized consensus almost surely for a fixed (connected) undirected graph; convergence time of the algorithm was studied in [13], with bounds on its expected value.”
Gossip Algorithms for Distributed Signal Processing (2010, methodology): “ion introduces new ﬁxed points other than consensus (Fig. 1(a)), and asynchronous gossiping in general reaches consensus, but without guarantees on the location of outcome (Fig. 1(b)). Kashyap et al. [65] ﬁrst considered a ﬁxed code quantized consensus algorithm, which preserves the network average ”
On quantized consensus by means of gossip algorithm - Part II: Convergence time (2009, background): “The notion of quantized consensus is defined in [18] for the case when quantized values (integers) are to be averaged over a connected network with digital communication channels.”
Distributed Consensus Algorithms in Sensor Networks: Quantized Data and Random Link Failures (2007, background): “These tradeoffs and vanishingly small mse contrasts with the algorithms in [20]–[24] where the mse is proportional to , the quantization step-size–if the step-size is large, these algorithms lead to a large mse.”
On Distributed Convex Optimization Under Inequality and Equality Constraints (2010, background): “Another related paper is [8] where a special case of [17], the network topology is ﬁxed and all the local constraint sets are identical, is addressed.”
Discrete-time dynamic average consensus (2010, background): “Key words: Consensus algorithms, multi-agent systems, cooperative control”
Stochastic consensus over noisy networks with Markovian and arbitrary switches (2010, background): “Kashyap, Basar, and Srikant (2007) developed randomized algorithms to achieve nearly average consensuswhere each node takes values from a set of integers.”
Distributed Parameter Estimation in Sensor Networks: Nonlinear Observation Models and Imperfect Communication (2008, background): “, [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], where in each time step only local averaging of the neighbors’ states occurs, and no observations are processed; and ii) distributed estimation algorithms, see recent literature,e.”
Convex Optimization in Signal Processing and Communications (2009, background): “This problem has recently gained interest in the networking literature [22, 12, 13, 31].”