Publications de El Mahdi El Mhamdi

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Machine Learning (ML) solutions are nowadays distributed, according to the so-called server/worker architecture. One server holds the model parameters while several workers train the model. Clearly, such architecture is prone to various types of component ...

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 ...

Could a gradient aggregation rule (GAR) for distributed machine learning be both robust and fast? This paper answers by the affirmative through Multi-Bulyan. Given n workers, f of which are arbitrary malicious (Byzantine) and m = n − f are not, we prove th ...

Whether it occurs in artificial or biological substrates, {\it learning} is a {distributed} phenomenon in at least two aspects. First, meaningful data and experiences are rarely found in one location, hence {\it learners} have a strong incentive to work t ...

The present invention concerns computer-implemented methods for training a machine learning model using Stochastic Gradient Descent, SGD. In one embodiment, the method is performed by a first computer in a distributed computing environment and comprises pe ...

Neural networks have been traditionally considered robust in the sense that their precision degrades gracefully with the failure of neurons and can be compensated by additional learning phases. Nevertheless, critical applications for which neural networks ...

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 ...

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 ...

Modern machine learning architectures distinguish servers and workers. Typically, a d-dimensional model is hosted by a server and trained by n workers, using a distributed stochastic gradient descent (SGD) optimization scheme. At each SGD step, the goal is ...

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 ...

Problems of pattern formation have been extensively studied in distributed computing. One of this problems is the gathering problem: agents must gather at a same position in a distributed manner. When gathering is not possible, a close problem is the conve ...

In reinforcement learning, agents learn by performing actions and observing their outcomes. Sometimes, it is desirable for a human operator to \textit{interrupt} an agent in order to prevent dangerous situations from happening. Yet, as part of their learni ...

Machine learning (ML) solutions are nowadays distributed, according to the so-called server/worker architecture. One server holds the model parameters while several workers train the model. Clearly, such architecture is prone to various types of component ...

Thin hydrogenated amorphous silicon (a-Si:H) films deposited on crystalline silicon (c-Si) surfaces are sensitive probes for the bulk electronic properties of a-Si:H. Here, we use such samples during repeated low-temperature annealing and visible-light soa ...

Asynchronous distributed machine learning solutions have proven very effective so far, but always assuming perfectly functioning workers. In practice, some of the workers can however exhibit Byzantine behavior, caused by hardware failures, software bugs, c ...

The geometric median, an instrumental component of the secure machine learning toolbox, is known to be effective when robustly aggregating models (or gradients), gathered from potentially malicious (or strategic) users. What is less known is the extent to ...

Byzantine-resilient Stochastic Gradient Descent (SGD) aims at shielding model training from Byzantine faults, be they ill-labeled training datapoints, exploited software/hardware vulnerabilities, or malicious worker nodes in a distributed setting. Two rece ...

The present application concerns a computer-implemented method for training a machine learning model in a distributed fashion, using Stochastic Gradient Descent, SGD, wherein the method is performed by a first computer in a distributed computing environmen ...

We address for the first time the problem of correcting group discriminations within a score function, while minimizing the individual error. Each group is described by a probability density function on the set of profiles. We first solve the problem analy ...

We report on \emph{Krum}, the first \emph{provably} Byzantine-tolerant aggregation rule for distributed Stochastic Gradient Descent (SGD). Krum guarantees the convergence of SGD even in a distributed setting where (asymptotically) up to half of the workers ...

El Mahdi El Mhamdi

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Genuinely Distributed Byzantine Machine Learning

On The Robustness of a Neural Network

Fast and Robust Distributed Learning in High Dimension

Robust Distributed Learning

Byzantine machine learning

When Neurons Fail - Technical Report

The Hidden Vulnerability of Distributed Learning in Byzantium

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

AKSEL: Fast Byzantine SGD

AGGREGATHOR: Byzantine Machine Learning via Robust Gradient Aggregation

Exploring The Borderlands Of The Gathering Problem

Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning

Genuinely distributed Byzantine machine learning

Is light-induced degradation of a-Si:H/c-Si interfaces reversible?

Asynchronous Byzantine Machine Learning (the case of SGD)

On the Strategyproofness of the Geometric Median

Distributed Momentum for Byzantine-resilient Stochastic Gradient Descent

Byzantine tolerant gradient descent for distributed machine learning with adversaries

Removing Algorithmic Discrimination (With Minimal Individual Error)

Brief Announcement: Byzantine-Tolerant Machine Learning