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research article

ARGO: Overcoming hardware dependence in distributed learning

Boubouh, Karim
•
Boussetta, Amine
•
Guerraoui, Rachid  
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July 1, 2025
Future Generation Computer Systems

Mobile devices offer a valuable resource for distributed learning alongside traditional computers, encouraging energy efficiency and privacy through local computations. However, the hardware limitations of these devices makes it impossible to use classical SGD for industry-grade machine learning models (with a very large number of parameters). Moreover, they are intermittently available and susceptible to failures. To address these challenges, we introduce ARGO, an algorithm that combines adaptive workload schemes with Byzantine resilience mechanisms, as well as dynamic device participation. Our theoretical analysis demonstrates linear convergence for strongly convex losses and sub-linear convergence for non-convex losses, without assuming specific dataset partitioning (for potential data heterogeneity). Our formal analysis highlights the interplay between convergence properties, hardware capabilities, Byzantine impact, and standard factors such as mini-batch size and learning rate. Through extensive evaluations, we show that ARGO outperforms standard SGD in terms of convergence speed and accuracy, and most importantly, thrives when classical SGD is not possible due to hardware limitations.

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Type
research article
DOI
10.1016/j.future.2025.107778
Scopus ID

2-s2.0-85219579134

Author(s)
Boubouh, Karim
•
Boussetta, Amine
•
Guerraoui, Rachid  
•
Maurer, Alexandre
Date Issued

2025-07-01

Published in
Future Generation Computer Systems
Volume

168

Article Number

107778

Subjects

Byzantine resilience

•

Distributed learning

•

Hardware heterogeneity

•

Stochastic optimization

Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
DCL  
Available on Infoscience
March 14, 2025
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/247822
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