In this article, we consider the problem of privacy preservation in average consensus when communication among nodes is quantized. More specifically, a setting where network can be curious, while certain want to ensure that their initial states cannot inferred exactly by these curious nodes. Curious are not malicious, i.e., they try identify other based on data receive during operation (and som...

In this paper, we study almost sure convergence of a dynamic average consensus algorithm which allows distributed computation of the product of n time-varying conditional probability density functions. These density functions (often called as “belief functions”) correspond to the conditional probability of observations given the state of an underlying Markov chain, which is observed by n differ...

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The authors have recently proposed a class of randomized gossip algorithms which solve the distributed averaging problem on directed graphs, with the constraint that each node has an integer-valued state. The essence of this algorithm is to maintain local records, called “surplus”, of individual state updates, thereby achieving quantized average consensus even though the state sum of all nodes ...

This paper considers the static average consensus problem for a multi-agent system and proposes a distributed algorithm that enables individual agents to set their own rate of convergence. The algorithm has a two-time scale structure and is constructed using a singular perturbation approach. A fast information processing state uses a Laplacian consensus strategy to calculate the agreement value...

The goal of the privacy-preserving average consensus (PPAC) is to guarantee the privacy of initial states and asymptotic consensus on the exact average of the initial value. This goal is achieved by an existing PPAC algorithm by adding and subtracting variance decaying and zero-sum random noises to the consensus process. However, there is lack of theoretical analysis to quantify the degree of t...

We address the problem of a set of agents reaching consensus by computing the average of their initial states. We propose two randomized algorithms over a directed communication graph where either a random node broadcast its value or a randomly selected pair of nodes communicate in a distributed fashion. The proposed algorithms guarantee convergence in three important definitions, namely: almos...

We consider the effect of interference on the convergence rate of a class of distributed averaging algorithms called consensus algorithms, which iteratively compute the measurement average by message passing among nodes. It is usually assumed that these algorithms converge faster with a greater exchange of information (i.e., by increased network connectivity) in every iteration. However, when t...

The k-set agreement problem is a generalization of the uniform consensus problem: each process proposes a value, and each non-faulty process has to decide a value such that a decided value is a proposed value, and at most k different values are decided. It has been shown that any algorithm that solves the k-set agreement problem in synchronous systems that can suffer up to t crash failures requ...