Sébastien Louis Alexandre Rouault

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AGGREGATHOR: Byzantine Machine Learning via Robust Gradient Aggregation

Rachid Guerraoui, El Mahdi El Mhamdi, Sébastien Louis Alexandre Rouault, Georgios Damaskinos, Arsany Hany Abdelmessih Guirguis

We present AGGREGATHOR, a framework that implements state-of-the-art robust (Byzantine-resilient) distributed stochastic gradient descent. Following the standard parameter server model, we assume that a minority of worker machines can be controlled by an a ...

2019

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

On The Robustness of a Neural Network

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

With the development of neural networks based machine learning and their usage in mission critical applications, voices are rising against the \textit{black box} aspect of neural networks as it becomes crucial to understand their limits and capabilities. W ...

2017

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