Quantized distributed load balancing with capacity constraints

Gravelle, Evan; Martínez, Sònia

doi:10.1109/cdc.2014.7039989

Cited by 9 publications

(13 citation statements)

References 13 publications

(17 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, in order for each agent to obtain a relative error ρ, the proposed algorithm iterates T (ρ) times as denoted in (29). Therefore, the total (expected) communication cost across all of the n agents is nT (ρ) · E |Code s (Q LP (x))| and nT (ρ) · E |Code ′ s (Q LP (x))| for small and large s, respectively.…”

Section: Optimal Quantization Level For Reducing Overall Communicamentioning

confidence: 99%

“…We also set δ = 0.45. Table V represents the total expected communication cost (in bits, as computed using (29), (30) and (31)) induced by the proposed algorithm to solve (32) using the low-precision quantizer -as described above-for four representative cases. As observed from this table and expected from the theoretical derivations, larger number of quantization levels translates to less noisy quantization and hence fewer iterations.…”

Section: Optimal Quantization Level For Reducing Overall Communicamentioning

confidence: 99%

See 1 more Smart Citation

An Exact Quantized Decentralized Gradient Descent Algorithm

Reisizadeh

Mokhtari

Hassani

et al. 2019

IEEE Trans. Signal Process.

106

View full text Add to dashboard Cite

We consider the problem of decentralized consensus optimization, where the sum of n smooth and strongly convex functions are minimized over n distributed agents that form a connected network. In particular, we consider the case that the communicated local decision variables among nodes are quantized in order to alleviate the communication bottleneck in distributed optimization. We propose the Quantized Decentralized Gradient Descent (QDGD) algorithm, in which nodes update their local decision variables by combining the quantized information received from their neighbors with their local information. We prove that under standard strong convexity and smoothness assumptions for the objective function, QDGD achieves a vanishing mean solution error under customary conditions for quantizers. To the best of our knowledge, this is the first algorithm that achieves vanishing consensus error in the presence of quantization noise. Moreover, we provide simulation results that show tight agreement between our derived theoretical convergence rate and the numerical results.

show abstract

Section: Optimal Quantization Level For Reducing Overall Communicamentioning

confidence: 99%

Section: Optimal Quantization Level For Reducing Overall Communicamentioning

confidence: 99%

An Exact Quantized Decentralized Gradient Descent Algorithm

Reisizadeh

Mokhtari

Hassani

et al. 2019

IEEE Trans. Signal Process.

106

View full text Add to dashboard Cite

show abstract

“…The system model in this section with delays also generalizes the models studied for quantized consensus in [1], [4], [6], [7], [16], [17], [25].…”

Section: ) Normal Networkmentioning

confidence: 99%

“…5(a). The updates follow the same deterministic rule in (17). The communication delays are present in the edges from agent 1 to its neighbors and are set as below: same as those in Fig.…”

Section: Numerical Examplementioning

confidence: 99%

“…Quantized consensus has been motivated by concerns on limited capabilities in communications and computations of the agents, and various studies have recently been carried out for the case without any malicious agents [1], [4], [6], [7], [16], [17], [19], [20], [22], [25], [29]. For enhancing resiliency, we employ a distributed update scheme in which each normal agent ignores its neighbors whose states appear extreme and unsafe in the sense that they differ the most from its own.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Resilient randomized quantized consensus

Dibaji

Ishii

Tempo

2016

2016 American Control Conference (ACC)

View full text Add to dashboard Cite

Abstract-We consider the problem of multi-agent consensus where some agents are subject to faults/attacks and might make updates arbitrarily. The network consists of agents taking integervalued (i.e., quantized) states under directed communication links. The goal of the healthy normal agents is to form consensus in their state values, which may be disturbed by the non-normal, malicious agents. We develop update schemes to be equipped by the normal agents whose interactions are asynchronous and subject to non-uniform and time-varying time delays. In particular, we employ a variant of the so-called mean subsequence reduced (MSR) algorithms, which have been long studied in computer science, where each normal agent ignores extreme values from its neighbors. We solve the resilient quantized consensus problems in the presence of totally/locally bounded adversarial agents and provide necessary and sufficient conditions in terms of the connectivity notion of graph robustness. Furthermore, it will be shown that randomization is essential both in quantization and in the updating times when normal agents interact in an asynchronous manner. The results are examined through a numerical example.

show abstract

Consensus in multi-agent systems: a review

2021

View full text Add to dashboard Cite

This paper provides a review of the consensus problem as one of the most challenging issues in the distributed control of the multi-agent systems (MASs). In this survey, firstly, the consensus algorithms for the agents with the single-integrator, double-integrator and high-order dynamic models were collected from various research works, and the convergence condition for each of these algorithms was explained. Secondly, all the consensus-related problems such as those in the sampled-data consensus, quantized consensus, random-network consensus, leader-follower consensus, finite-time consensus, bipartite consensus, group consensus/cluster consensus, and the scaled consensus were analyzed and compared with each other. Thirdly, we focused on the common control techniques used for the consensus problems in the presence of disturbance and divided all these control methods into two categories: robust control and adaptive control. Finally, we reviewed the most prevalent consensus applications in the MASs, including the subjects of rendezvous, formation control, axial alignment and the wireless sensor networks.

show abstract

Quantized distributed load balancing with capacity constraints

Cited by 9 publications

References 13 publications

An Exact Quantized Decentralized Gradient Descent Algorithm

An Exact Quantized Decentralized Gradient Descent Algorithm

Resilient randomized quantized consensus

Consensus in multi-agent systems: a review

Contact Info

Product

Resources

About