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Found 14 papers, 0 papers with code
MaxCUCL: Max-Consensus with Deterministic Convergence in Networks with Unreliable Communication
Our proposed algorithm is the first algorithm that achieves max-consensus in a deterministic manner (i. e., nodes always calculate the maximum of their states regardless of the nature of the probability distribution of the packet drops).
Survey of Distributed Algorithms for Resource Allocation over Multi-Agent Systems
This survey paper provides a comprehensive analysis of distributed algorithms for addressing the distributed resource allocation (DRA) problem over multi-agent systems.
Distributed Optimization via Gradient Descent with Event-Triggered Zooming over Quantized Communication
Distributed methods in which nodes use quantized communication yield a solution at the proximity of the optimal solution, hence reaching an error floor that depends on the quantization level used; the finer the quantization the lower the error floor.
Online Distributed Learning with Quantized Finite-Time Coordination
Online distributed learning refers to the process of training learning models on distributed data sources.
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.
Distributed Optimization with Quantized Gradient Descent
For solving this distributed optimization problem, we combine a gradient descent method with a distributed quantized consensus algorithm (which requires the nodes to exchange quantized messages and converges in a finite number of steps).
Distributed CPU Scheduling Subject to Nonlinear Constraints
This paper considers a network of collaborating agents for local resource allocation subject to nonlinear model constraints.
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.
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).
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.
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 Optimal Allocation with Quantized Communication and Privacy-Preserving Guarantees
It is shown that the proposed privacy-preserving resource allocation algorithm performs well with an appropriate convergence rate under privacy guarantees.
