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Hauptverfasser: Kuwaranancharoen, Kananart, Sundaram, Shreyas
Format: Preprint
Veröffentlicht: 2023
Schlagworte:
Online-Zugang:https://arxiv.org/abs/2305.10810
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author Kuwaranancharoen, Kananart
Sundaram, Shreyas
author_facet Kuwaranancharoen, Kananart
Sundaram, Shreyas
contents The problem of designing distributed optimization algorithms that are resilient to Byzantine adversaries has received significant attention. For the Byzantine-resilient distributed optimization problem, the goal is to (approximately) minimize the average of the local cost functions held by the regular (non adversarial) agents in the network. In this paper, we provide a general algorithmic framework for Byzantine-resilient distributed optimization which includes some state-of-the-art algorithms as special cases. We analyze the convergence of algorithms within the framework, and derive a geometric rate of convergence of all regular agents to a ball around the optimal solution (whose size we characterize). Furthermore, we show that approximate consensus can be achieved geometrically fast under some minimal conditions. Our analysis provides insights into the relationship among the convergence region, distance between regular agents' values, step-size, and properties of the agents' functions for Byzantine-resilient distributed optimization.
format Preprint
id arxiv_https___arxiv_org_abs_2305_10810
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle On the Geometric Convergence of Byzantine-Resilient Distributed Optimization Algorithms
Kuwaranancharoen, Kananart
Sundaram, Shreyas
Optimization and Control
Multiagent Systems
52A41, 93A14, 93A16, 90C25, 90C35, 65K05, 68M15, 68W15, 68W40
C.2.4; G.1.6; B.4.5
The problem of designing distributed optimization algorithms that are resilient to Byzantine adversaries has received significant attention. For the Byzantine-resilient distributed optimization problem, the goal is to (approximately) minimize the average of the local cost functions held by the regular (non adversarial) agents in the network. In this paper, we provide a general algorithmic framework for Byzantine-resilient distributed optimization which includes some state-of-the-art algorithms as special cases. We analyze the convergence of algorithms within the framework, and derive a geometric rate of convergence of all regular agents to a ball around the optimal solution (whose size we characterize). Furthermore, we show that approximate consensus can be achieved geometrically fast under some minimal conditions. Our analysis provides insights into the relationship among the convergence region, distance between regular agents' values, step-size, and properties of the agents' functions for Byzantine-resilient distributed optimization.
title On the Geometric Convergence of Byzantine-Resilient Distributed Optimization Algorithms
topic Optimization and Control
Multiagent Systems
52A41, 93A14, 93A16, 90C25, 90C35, 65K05, 68M15, 68W15, 68W40
C.2.4; G.1.6; B.4.5
url https://arxiv.org/abs/2305.10810