Search Results for author:
Found 11 papers, 0 papers with code
Global and Local Error-Tolerant Decentralized State Estimation under Partially Ordered Observations
Two types of errors, global errors and local errors, are proposed to describe the impact of errors on decentralized information processing.
Distributed Optimization for Quadratic Cost Functions over Large-Scale Networks with Quantized Communication and Finite-Time Convergence
We prove that our algorithms converge in a finite number of iterations to the exact optimal solution depending on the quantization level, and we present applications of our algorithms to (i) optimal task scheduling for data centers, and (ii) global model aggregation for distributed federated learning.
Distributed Computation of Exact Average Degree and Network Size in Finite Number of Steps under Quantized Communication
We present two distributed algorithms which rely on quantized operation (i. e., nodes process and transmit quantized messages), and are able to calculate the exact solutions in a finite number of steps.
ARQ-based Average Consensus over Unreliable Directed Network Topologies
In this paper, we address the discrete-time average consensus problem, where nodes exchange information over unreliable communication links.
Distributed CPU Scheduling Subject to Nonlinear Constraints
This paper considers a network of collaborating agents for local resource allocation subject to nonlinear model constraints.
Finite Time Privacy Preserving Quantized Average Consensus with Transmission Stopping
Furthermore, we present topological conditions under which the proposed algorithm allows nodes to preserve their privacy.
Optimal Database Allocation in Finite Time with Efficient Communication and Transmission Stopping over Dynamic Networks
In this paper, we focus on the problem of data sharing over a wireless computer network (i. e., a wireless grid).
Distributed Finite Time k-means Clustering with Quantized Communucation and Transmission Stopping
The goal of $k$-means is to partition the network's agents in mutually exclusive sets (groups) such that agents in the same set have (and possibly share) similar information and are able to calculate a representative value for their group. During the operation of our distributed algorithm, each node (i) transmits quantized values in an event-driven fashion, and (ii) exhibits distributed stopping capabilities.
Privacy-Preserving Distributed Average Consensus in Finite Time using Random Gossip
In this paper, we develop and analyze a gossip-based average consensus algorithm that enables all of the components of a distributed system, each with some initial value, to reach (approximate) average consensus on their initial values after executing a finite number of iterations, and without having to reveal the specific value they contribute to the average calculation.
Finite Time Exact Quantized Average Consensus with Limited Resources and Transmission Stopping for Energy-Aware Networks
Motivated by these novel requirements, in this paper, we present and analyze a novel distributed average consensus algorithm, which (i) operates exclusively on quantized values (in order to guarantee efficient communication and data storage), and (ii) relies on event-driven updates (in order to reduce energy consumption, communication bandwidth, network congestion, and/or processor usage).
Distributed k-means algorithm
Since the partitions may not have a relation with the topology of the network--members of the same clusters may not be spatially close--the algorithm is provided with a mechanism to compute the clusters'centroids even when the clusters are disconnected in several sub-clusters. The results of the proposed distributed algorithm coincide, in terms of minimization of the objective function, with the centralized k-means algorithm.
