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Anne-Marie Kermarrec

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Publications associées (63)

FLEET: Online Federated Learning via Staleness Awareness and Performance Prediction

Rachid Guerraoui, Anne-Marie Kermarrec, Rhicheek Patra

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 ...

ASSOC COMPUTING MACHINERY2022

D-Cliques: Compensating for Data Heterogeneity with Topology in Decentralized Federated Learning

Anne-Marie Kermarrec, Erick Lavoie

The convergence speed of machine learning models trained with Federated Learning is significantly affected by non-independent and identically distributed (non-IID) data partitions, even more so in a fully decentralized setting without a central server. In ...

2022

G-Fake: Tell Me How It is Shared and I Shall Tell You If It is Fake

Rachid Guerraoui, Anne-Marie Kermarrec

The propagation of fake news is an increasingly serious concern in social platforms, and designing methods to automatically detect them and limit their spread is an important research challenge. Most existing methods rely on inspecting the content of news ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

Frugal Decentralized Learning

Anne-Marie Kermarrec

Machine learning is currently shifting from a centralized paradigm to decentralized ones where machine learning models are trained collaboratively. In fully decentralized learning algorithms, data remains where it was produced, models are trained locally a ...

IEEE2022

Cluster-and-Conquer: When Randomness Meets Graph Locality

Anne-Marie Kermarrec, Olivier Ruas

K-Nearest-Neighbors (KNN) graphs are central to many emblematic data mining and machine-learning applications. Some of the most efficient KNN graph algorithms are incremental and local: they start from a random graph, which they incrementally improve by tr ...

IEEE COMPUTER SOC2021

Quicker ADC : Unlocking the Hidden Potential of Product Quantization With SIMD

Anne-Marie Kermarrec

Efficient Nearest Neighbor (NN) search in high-dimensional spaces is a foundation of many multimedia retrieval systems. A common approach is to rely on Product Quantization, which allows the storage of large vector databases in memory and efficient distanc ...

IEEE COMPUTER SOC2021

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

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

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

The Fake News Vaccine

Rachid Guerraoui, Willy Zwaenepoel, Anne-Marie Kermarrec, Oana Maria Balmau, Matej Pavlovic, Alexandre David Olivier Maurer

While spreading fake news is an old phenomenon, today social media enables misinformation to instantaneously reach millions of people. Content-based approaches to detect fake news, typically based on automatic text checking, are limited. It is indeed diffi ...

2019

Fingerprinting Big Data: The Case of KNN Graph Construction

Rachid Guerraoui, Anne-Marie Kermarrec, Olivier Ruas

We propose fingerprinting, a new technique that consists in constructing compact, fast-to-compute and privacy-preserving binary representations of datasets. We illustrate the effectiveness of our approach on the emblematic big data problem of K-Nearest-Nei ...

2019

Collaborative Filtering Under a Sybil Attack: Similarity Metrics do Matter!

Rachid Guerraoui, Anne-Marie Kermarrec, Antoine Rault, Florestan Laurentin Marie De Moor, Davide Frey

Recommendation systems help users identify interesting content, but they also open new privacy threats. In this paper, we deeply analyze the effect of a Sybil attack that tries to infer information on users from a user-based collaborative-filtering recomme ...

IEEE2018

The Utility and Privacy Effects of a Click

Rachid Guerraoui, Anne-Marie Kermarrec, Mahsa Taziki

2017

Heterogeneous Recommendations: What You Might Like To Read After Watching Interstellar

Rachid Guerraoui, Anne-Marie Kermarrec, Rhicheek Patra, Tao Lin

Recommenders, as widely implemented nowadays by major e-commerce players like Netflix or Amazon, use collaborative filtering to suggest the most relevant items to their users. Clearly, the effectiveness of recommenders depends on the data they can exploit, ...

Assoc Computing Machinery2017

ProteusTM: Abstraction Meets Performance in Transactional Memory

Rachid Guerraoui, Anne-Marie Kermarrec, Diego Didona

The Transactional Memory (TM) paradigm promises to greatly simplify the development of concurrent applications. This led, over the years, to the creation of a plethora of TM implementations delivering wide ranges of performance across workloads. Yet, no un ...

Assoc Computing Machinery2016

Privacy-preserving distributed collaborative filtering

Rachid Guerraoui, Anne-Marie Kermarrec, Davide Frey

We propose a new mechanism to preserve privacy while leveraging user profiles in distributed recommender systems. Our mechanism relies on two contributions: (i) an original obfuscation scheme, and (ii) a randomized dissemination protocol. We show that our ...

Springer Wien2016

ProteusTM: Abstraction Meets Performance in Transactional Memory

Rachid Guerraoui, Anne-Marie Kermarrec, Paolo Romano, Diego Didona

2016

D2P: Distance-Based Differential Privacy in Recommenders

Rachid Guerraoui, Anne-Marie Kermarrec, Rhicheek Patra, Mahsa Taziki

The upsurge in the number of web users over the last two decades has resulted in a significant growth of online information. This information growth calls for recommenders that personalize the in- formation proposed to each individual user. Nevertheless, p ...

Assoc Computing Machinery2015

Hawk: Hybrid Datacenter Scheduling

Willy Zwaenepoel, Florin Dinu, Anne-Marie Kermarrec, Pamela Isabel Delgado Borda

This paper addresses the problem of efficient scheduling of large clusters under high load and heterogeneous workloads. A heterogeneous workload typically consists of many short jobs and a small number of large jobs that consume the bulk of the cluster's r ...

USENIX Association2015

Personalizing Top-k Processing Online in a Peer-to-Peer Social Tagging Network

Rachid Guerraoui, Anne-Marie Kermarrec

The rapidly increasing amount of user-generated content in social tagging systems provides a huge source of information. Yet, performing effective search in these systems is very challenging, especially when we seek the most appropriate items that match a ...

Assoc Computing Machinery2014

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