Publications by Julien Michel Stainer

Byzantine tolerant gradient descent for distributed machine learning with adversaries

Rachid Guerraoui, El Mahdi El Mhamdi, Peva François Blanchard, Julien Michel Stainer

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 ...

2019

The entropy of a distributed computation random number generation from memory interleaving

Patrick Blanchard, Rachid Guerraoui, Karolos Antoniadis, Julien Michel Stainer

2018

The Disclosure Power of Shared Objects

Rachid Guerraoui, Mihail Igor Zablotchi, Peva François Blanchard, Julien Michel Stainer

Shared objects are the means by which processes gather and exchange information about the state of a distributed system. Objects that disclose more information about the system are therefore more desirable. In this paper, we propose the schedule reconstruc ...

2017

Sequential Proximity: Towards Provably Scalable Concurrent Search Algorithms

Rachid Guerraoui, Vasileios Trigonakis, Karolos Antoniadis, Julien Michel Stainer

Establishing the scalability of a concurrent algorithm a priori, before implementing and evaluating it on a concrete multi-core platform, seems difficult, if not impossible. In the context of search data structures however, according to all practical work ...

Springer International Publishing2017

Brief Announcement: Byzantine-Tolerant Machine Learning

Rachid Guerraoui, El Mahdi El Mhamdi, Peva François Blanchard, Julien Michel Stainer

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 ...

ACM2017

Trust-aware peer sampling: Performance and privacy tradeoffs

Anne-Marie Kermarrec, Julien Michel Stainer, Davide Frey

The ability to identify people that share one's own interests is one of the most interesting promises of the Web 2.0 driving user-centric applications such as recommendation systems or collaborative marketplaces. To be truly useful, however, information ab ...

Elsevier Science Bv2013