no code implementations • 28 Feb 2024 • Apostolos I. Rikos, Themistoklis Charalambous, Karl H. Johansson
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).
no code implementations • 28 Jan 2024 • Mohammadreza Doostmohammadian, Alireza Aghasi, Mohammad Pirani, Ehsan Nekouei, Houman Zarrabi, Reza Keypour, Apostolos I. Rikos, Karl H. Johansson
This survey paper provides a comprehensive analysis of distributed algorithms for addressing the distributed resource allocation (DRA) problem over multi-agent systems.
no code implementations • 8 Sep 2023 • Apostolos I. Rikos, Wei Jiang, Themistoklis Charalambous, Karl H. Johansson
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.
no code implementations • 13 Jul 2023 • Nicola Bastianello, Apostolos I. Rikos, Karl H. Johansson
Online distributed learning refers to the process of training learning models on distributed data sources.
no code implementations • 2 Apr 2023 • Apostolos I. Rikos, Andreas Grammenos, Evangelia Kalyvianaki, Christoforos N. Hadjicostis, Themistoklis Charalambous, Karl H. Johansson
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.
no code implementations • 29 Nov 2022 • Apostolos I. Rikos, Themistoklis Charalambous, Christoforos N. Hadjicostis, Karl H. Johansson
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.
no code implementations • 20 Nov 2022 • Apostolos I. Rikos, Wei Jiang, Themistoklis Charalambous, Karl H. Johansson
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).
no code implementations • 30 Aug 2022 • Mohammadreza Doostmohammadian, Alireza Aghasi, Apostolos I. Rikos, Andreas Grammenos, Evangelia Kalyvianaki, Christoforos N. Hadjicostis, Karl H. Johansson, Themistoklis Charalambous
This paper considers a network of collaborating agents for local resource allocation subject to nonlinear model constraints.
no code implementations • 17 Jul 2022 • Apostolos I. Rikos, Gabriele Oliva, Christoforos N. Hadjicostis, Karl H. Johansson
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.
no code implementations • 17 Jul 2022 • Apostolos I. Rikos, Christoforos N. Hadjicostis, Karl H. Johansson
In this paper, we focus on the problem of data sharing over a wireless computer network (i. e., a wireless grid).
no code implementations • 17 Jul 2022 • Apostolos I. Rikos, Christoforos N. Hadjicostis, Karl H. Johansson
Furthermore, we present topological conditions under which the proposed algorithm allows nodes to preserve their privacy.
no code implementations • 8 Nov 2021 • Nicolaos E. Manitara, Apostolos I. Rikos, Christoforos N. Hadjicostis
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.
no code implementations • 1 Oct 2021 • Apostolos I. Rikos, Christoforos N. Hadjicostis, Karl H. Johansson
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).
no code implementations • 29 Sep 2021 • Jakob Nylöf, Apostolos I. Rikos, Sebin Gracy, Karl H. Johansson
It is shown that the proposed privacy-preserving resource allocation algorithm performs well with an appropriate convergence rate under privacy guarantees.