Two for the price of one: communication efficient and privacy-preserving distributed average consensus using quantization

Both communication overhead and privacy are main concerns in designing distributed computing algorithms. It is very challenging to address them simultaneously as encryption methods required for privacy-preservation often incur high communication costs. In this paper, we argue that there is a fundamental link between communication efficiency...

A Privacy-Preserving Asynchronous Averaging Algorithm based on State Decomposition

Average consensus algorithms are used in many distributed systems such as distributed optimization, sensor fusion and the control of dynamic systems. Consensus algorithms converge through an explicit exchange of state variables. In some cases, however, the state variables are confidential. In this paper, a privacy-preserving asynchronous...

Convex Optimisation-Based Privacy-Preserving Distributed Average Consensus in Wireless Sensor Networks

In many applications of wireless sensor networks, it is important that the privacy of the nodes of the network be protected. Therefore, privacy-preserving algorithms have received quite some attention recently. In this paper, we propose a novel convex optimization-based solution to the problem of privacy-preserving distributed average consensus....

Privacy-preserving consensus averaging

Consensus problem has been a topic of interest for many research areas allowing multiple agents to reach an agreement through local information exchange. The explicit share of the state variables, however, may cause privacy issues due to the confidentiality of the initial values. In this work, asynchronous privacy-preserving consensus average...

On the Limits of Finite-Time Distributed Consensus Through Successive Local Linear Operations

In this work, we explore the limits of finite-time distributed consensus through the intersection of graph filters and matrix function theory. We focus on algorithms capable to compute the consensus exactly through filtering operations over a graph, and that have been proven to converge in finite time. In this context, we show that there...

Quantization effects in PDMM: A first study for synchronous distributed averaging

Nowadays, large-scale networks of computing units, often characterized by the absence of central control, have become more commonplace in many applications. To facilitate data processing in these large-scale networks distributed signal processing is required. The iterative behaviour of distributed processing algorithms combined with the limited...