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Rachid Guerraoui

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Publications associées (292)

Differentially Private Stochastic Coordinate Descent

Rachid Guerraoui, Georgios Damaskinos

In this paper we tackle the challenge of making the stochastic coordinate descent algorithm differentially private. Compared to the classical gradient descent algorithm where updates operate on a single model vector and controlled noise addition to this ve ...

AIAA2021

Distributed Momentum for Byzantine-resilient Stochastic Gradient Descent

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

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 ...

2021

Probabilistic and temporal failure detectors for solving distributed problems

Rachid Guerraoui

Failure detectors (FD)s are celebrated for their modularity in solving distributed problems. Algorithms are constructed using FD building blocks. Synchrony assumptions to implement FDs are studied separately and are typically expressed as eventual guarante ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2021

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

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

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

The Imitation Game: Algorithm Selection by Exploiting Black-Box Recommenders

Rachid Guerraoui, Georgios Damaskinos

Cross-validation is commonly used to select the recommendation algorithms that will generalize best on yet unknown data. Yet, in many situations the available dataset used for cross-validation is scarce and the selected algorithm might not be the best suit ...

Springer2021

Dynamic Byzantine Reliable Broadcast

Rachid Guerraoui, Dragos-Adrian Seredinschi, Jovan Komatovic, 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

AKSEL: Fast Byzantine SGD

Rachid Guerraoui, El Mahdi El Mhamdi, Alexandre David Olivier Maurer, Sébastien Louis Alexandre Rouault

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 ...

Schloss Dagstuhl--Leibniz-Zentrum für Informatik2021

Leaderless Consensus

Rachid Guerraoui, Vincent Gramoli, Mihail Igor Zablotchi, Karolos Antoniadis, Antoine Philippe Matthieu Desjardins

Classical synchronous consensus algorithms are leaderless: processes exchange their proposals, retain the maximum value and decide when they see the same choice across a couple of rounds. Indulgent consensus algorithms are more robust in that they only req ...

IEEE COMPUTER SOC2021

Differential Privacy and Byzantine Resilience in SGD: Do They Add Up?

Rachid Guerraoui, Sébastien Louis Alexandre Rouault, John Stephan, Nirupam Gupta, Rafaël Benjamin Pinot

This paper addresses the problem of combining Byzantine resilience with privacy in machine learning (ML). Specifically, we study if a distributed implementation of the renowned Stochastic Gradient Descent (SGD) learning algorithm is feasible with both diff ...

ASSOC COMPUTING MACHINERY2021

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

FeGAN: Scaling Distributed GANs

Rachid Guerraoui, Anne-Marie Kermarrec, Arsany Hany Abdelmessih Guirguis

Existing approaches to distribute Generative Adversarial Networks (GANs) either (i) fail to scale for they typically put the two components of a GAN (the generator and the discriminator) on different machines, inducing significant communication overhead, o ...

2020

The Cost of Scaling a Reliable Interconnection Topology

Rachid Guerraoui, Alexandre David Olivier Maurer

In distributed computing, many papers try to evaluate the message complexity of a distributed system as a function of the number of nodes n. But what about the cost of building the distributed system itself? Assuming that we want to reliably connect n node ...

2020

Who Started This Rumor? Quantifying the Natural Differential Privacy of Gossip Protocols

Rachid Guerraoui, Hadrien Hendrikx

Gossip protocols (also called rumor spreading or epidemic protocols) are widely used to disseminate information in massive peer-to-peer networks. These protocols are often claimed to guarantee privacy because of the uncertainty they introduce on the node t ...

Schloss Dagstuhl, Leibniz-Zentrum2020

Online Payments by Merely Broadcasting Messages

Rachid Guerraoui, Matej Pavlovic, Dragos-Adrian Seredinschi, Matteo Monti, Athanasios Xygkis, Jovan Komatovic, Daniel Patrick Collins, Andrei Tonkikh

We address the problem of online payments, where users can transfer funds among themselves. We introduce Astro, a system solving this problem efficiently in a decentralized, deterministic, and completely asynchronous manner. Astro builds on the insight tha ...

IEEE2020

Thread-Placement Learning

Rachid Guerraoui, Vasileios Trigonakis, Karolos Antoniadis

In a non-uniform memory access machine, the placement of software threads to hardware cores can have a significant effect on the performance of concurrent applications. Detecting the best possible placement for each application is a necessity for thread sc ...

IEEE COMPUTER SOC2020

Genuinely Distributed Byzantine Machine Learning

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

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 ...

Association for Computing Machinery2020

Smaller, Faster & Lighter KNN Graph Constructions

Rachid Guerraoui, Anne-Marie Kermarrec, Olivier Ruas

We propose GoldFinger, a new compact and fast-to-compute binary representation of datasets to approximate Jaccard’s index. We illustrate the effectiveness of GoldFinger on the emblematic big data problem of K-Nearest-Neighbor (KNN) graph construction and s ...

2020

Fast and Robust Distributed Learning in High Dimension

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

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 ...

2020

The Impossibility of Fast Transactions

Rachid Guerraoui, Karolos Antoniadis

We prove that transactions cannot be fast in an asynchronous fault-tolerant system. Our result holds in any system where we require transactions to ensure monotonic writes, or any stronger consistency model, such as, causal consistency. Thus, our result un ...

IEEE2020

FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction

Rachid Guerraoui, Anne-Marie Kermarrec, Rhicheek Patra, Georgios Damaskinos, Vlad-Tiberiu Nitu

Federated Learning (FL) is very appealing for its privacy benefits: essentially, a global model is trained with updates computed on mobile devices while keeping the data of users local. Standard FL infrastructures are however designed to have no energy or ...

Association for Computing Machinery2020

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