Publications associées à Art byzantin

Fixing by Mixing: A Recipe for Optimal Byzantine ML under Heterogeneity

Rachid Guerraoui, John Stephan, Sadegh Farhadkhani, Youssef Allouah, Nirupam Gupta, Rafaël Benjamin Pinot

Byzantine machine learning (ML) aims to ensure the resilience of distributed learning algorithms to misbehaving (or Byzantine) machines. Although this problem received significant attention, prior works often assume the data held by the machines to be homo ...

PMLR2023

Byzantine Machine Learning Made Easy By Resilient Averaging of Momentums

Rachid Guerraoui, John Stephan, Sadegh Farhadkhani, Nirupam Gupta, Rafaël Benjamin Pinot

Byzantine resilience emerged as a prominent topic within the distributed machine learning community. Essentially, the goal is to enhance distributed optimization algorithms, such as distributed SGD, in a way that guarantees convergence despite the presence ...

PMLR2022

An Equivalence Between Data Poisoning and Byzantine Gradient Attacks

Rachid Guerraoui, Sadegh Farhadkhani, Le Nguyen Hoang, Oscar Jean Olivier Villemaud

To study the resilience of distributed learning, the “Byzantine" literature considers a strong threat model where workers can report arbitrary gradients to the parameter server. Whereas this model helped obtain several fundamental results, it has sometimes ...

PMLR2022

GARFIELD: System Support for Byzantine Machine Learning (Regular Paper)

Rachid Guerraoui, Sébastien Louis Alexandre Rouault, Arsany Hany Abdelmessih Guirguis

We present GARFIELD, a library to transparently make machine learning (ML) applications, initially built with popular (but fragile) frameworks, e.g., TensorFlow and PyTorch, Byzantine–resilient. GARFIELD relies on a novel object–oriented design, reducing t ...

IEEE2021

Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning)

Rachid Guerraoui, Sadegh Farhadkhani, El Mahdi El Mhamdi, Le Nguyen Hoang, Sébastien Louis Alexandre Rouault, Arsany Hany Abdelmessih Guirguis

We study Byzantine collaborative learning, where n nodes seek to collectively learn from each others' local data. The data distribution may vary from one node to another. No node is trusted, and f < n nodes can behave arbitrarily. We prove that collaborati ...

2021

Dynamic Byzantine Reliable Broadcast

Rachid Guerraoui, Jovan Komatovic, Dragos-Adrian Seredinschi, Andrei Tonkikh

Reliable broadcast is a communication primitive guaranteeing, intuitively, that all processes in a distributed system deliver the same set of messages. The reason why this primitive is appealing is twofold: (i) we can implement it deterministically in a co ...

Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik2021

Scalable Byzantine Reliable Broadcast

Rachid Guerraoui, Dragos-Adrian Seredinschi, Matej Pavlovic, Matteo Monti

Byzantine reliable broadcast is a powerful primitive that allows a set of processes to agree on a message from a designated sender, even if some processes (including the sender) are Byzantine. Existing broadcast protocols for this setting scale poorly, as ...

Schloss Dagstuhl--Leibniz-Zentrum fer Informatik2019

The Hidden Vulnerability of Distributed Learning in Byzantium

Rachid Guerraoui, El Mahdi El Mhamdi, Sébastien Louis Alexandre Rouault

While machine learning is going through an era of celebrated success, concerns have been raised about the vulnerability of its backbone: stochastic gradient descent (SGD). Recent approaches have been proposed to ensure the robustness of distributed SGD aga ...

2018

Consensus when all processes may be Byzantine for some time

Martin Hutle, Martin Biely

Among all classes of faults, Byzantine faults form the most general modeling of value faults. Traditionally, in the Byzantine fault model, faults are statically attributed to a set of up to t processes. This, however, implies that in this model a process a ...

2011