Quantized average consensus via dynamic coding/decoding schemes

By Ruggero Carli, Francesco Bullo, Sandro Zampieri. 2008

217 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Molecular Biology

How researchers see this paper

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

In Computer Networks and Communications

In Molecular Biology

Citations in the corpus

Decentralized Deep Learning with Arbitrary Communication Compression (2019, background): “It has been observed that gossip averaging can diverge (or not converge to the correct solution) in the presence of quantization noise (Xiao et al., 2005; Carli et al., 2007; Nedić et al., 2008; Dimakis et al., 2010; Carli et al., 2010b; Yuan et al., 2012).”
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication (2019, methodology): “The proposed schemes in (Carli et al., 2007; Nedi´c et al., 2008; Aysal et al., 2008; Carli et al., 2010b; Yuan et al., 2012) do only converge to a neighborhood (whose size depends on the compression accuracy) of the solution.”
Gossip consensus algorithms via quantized communication (2009, background): “Actually, such approach to consensus algorithm with quantized communication has been undertaken in [3], assuming a static communication network and logarithmic or zooming-in/zooming-out quantizers.”
On the Convergence Time of Asynchronous Distributed Quantized Averaging Algorithms (2010, background): “Prior work on distributed quantized averaging over fixed graphs includes [1], [6], [7], [12].”
Discontinuities and hysteresis in quantized average consensus (2010, unknown): “A number of publications have discussed –mostly in discretetime systems– several options to deal with the quantization constraint, namely uniform deterministic quantizers [13,18], uniform randomized quantizers [2], logarithmic quantizers [5], and adaptive quantizers [17].”
Distributed robust consensus control of multi-agent systems with heterogeneous matching uncertainties (2013, background): “Consensus algorithms are designed in [8,17] for multi-agent systems with quantized communication links.”
Distributed consensus of linear multi-agent systems with adaptive dynamic protocols (2011, unknown): “Consensus algorithms are designed in [7, 13] for a group of agents with quantized communication links and limited data rate.”
Consensus of Multi-Agent Systems With General Linear and Lipschitz Nonlinear Dynamics Using Distributed Adaptive Protocols (2011, background): “Consensus algorithms are designed in [8], [9] for a group of agents with quantized communication links and limited data rate.”
Distributed Average Consensus With Quantization Refinement (2013, methodology): “The quantization threshold in [16] is adapted online based on a zoom-in zoom-out strategy, while the set of quantization levels is maintained constant over the iterations.”
Network Topology and Communication Data Rate for Consensusability of Discrete-Time Multi-Agent Systems (2011, background): “We note that quantized average consensus problem on the ﬁ rst-order systems has been intensively studied in recent years, see [4], [6], [18] and the references therein.”
Distributed averaging on digital erasure networks (2011, methodology): “This behavior has been confirmed in a number of simulations we have run implementing the algorithms, an example of which is reported in Carli et al. (2009). Furthermore, in Theorem 4, both complexities depend on ρ, the second largest singular value of thematrix P .”