Publications de Anne-Marie Kermarrec

Publications associées (63)

Computing in social networks

Rachid Guerraoui, Anne-Marie Kermarrec, Kévin Clément Huguenin, Andrei Giurgiu

This paper defines the problem of Scalable Secure computing in a Social network: we call it the S-3 problem. In short, nodes, directly reflecting on associated users, need to compute a symmetric function f : V-n -> U of their inputs in a set of constant si ...

Elsevier2014

Rethinking Personalized Client Collaboration in Federated Learning

Anne-Marie Kermarrec

Federated Learning (FL) has gained considerable attention recently, as it allows clients to cooperatively train a global machine learning model without sharing raw data. However, its performance can be compromised due to the high heterogeneity in clients' ...

2024

GosSkip, an Efficient, Fault-Tolerant and Self Organizing Overlay Using Gossip-based Construction and Skip-Lists principles

Rachid Guerraoui, Anne-Marie Kermarrec, Kévin Clément Huguenin, Sidath Handurukande

This paper presents GosSkip, a self organizing and fully distributed overlay that provides a scalable support to data storage and retrieval in dynamic environments. The structure of GosSkip, while initially possibly chaotic, eventually matches a perfect se ...

2006

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

FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction

Rachid Guerraoui, Anne-Marie Kermarrec, Rhicheek Patra, Georgios Damaskinos

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

Practical Federated Learning without a Server

Anne-Marie Kermarrec, Rishi Sharma, Marinus Abraham de Vos, Akash Balasaheb Dhasade, Erick Lavoie

Federated Learning (FL) enables end-user devices to collaboratively train ML models without sharing raw data, thereby preserving data privacy. In FL, a central parameter server coordinates the learning process by iteratively aggregating the trained models ...

ACM2025

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

Rachid Guerraoui, Anne-Marie Kermarrec, Antoine Rault

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

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

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

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

Trust-aware peer sampling: Performance and privacy tradeoffs

Anne-Marie Kermarrec, Julien Michel Stainer

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

Tracking freeriders in gossip-based content dissemination systems

Rachid Guerraoui, Anne-Marie Kermarrec, Kévin Clément Huguenin, Maxime Monod

Gossip-based protocols have proven very efficient for disseminating high-bandwidth content such as video streams in a peer-to-peer fashion. However, for the protocols to work, nodes are required to collaborate by devoting a fraction of their upload bandwid ...

Elsevier2014

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

Hawk: Hybrid Datacenter Scheduling

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

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

Decentralized Polling with Respectable Participants

Rachid Guerraoui, Anne-Marie Kermarrec, Kévin Clément Huguenin, Maxime Monod

We consider the polling problem in a social network where participants care about their reputation: they do not want their vote to be disclosed nor their misbehaving, if any, to be publicly exposed. Assuming this reputation concern, we show that a simple s ...

2009

SONDe, a Self-Organizing Object Deployment Algorithm in Large-Scale Dynamic Systems

Anne-Marie Kermarrec, Vincent Gramoli

We present the design, correctness, and analysis of SONDe, a simple fully decentralized object deployment algorithm for highly requested systems. Given an object (service or data), SONDe provides a node with a constant upper bound (h) on the number of logi ...

2008

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

Fair Decentralized Learning

Anne-Marie Kermarrec, Rishi Sharma, Marinus Abraham de Vos, Sayan Biswas

Decentralized learning (DL) is an emerging approach that enables nodes to collaboratively train a machine learning model without sharing raw data. In many application domains, such as healthcare, this approach faces challenges due to the high level of hete ...

IEEE2025

ProteusTM: Abstraction Meets Performance in Transactional Memory

Rachid Guerraoui, Anne-Marie Kermarrec, Paolo Romano, 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 ...

2016

Boosting Resource-Constrained Federated Learning Systems with Guessed Updates

Mohamed Yassine Boukhari, Anne-Marie Kermarrec, Rafael Pereira Pires, Rishi Sharma, Akash Balasaheb Dhasade

Federated learning (FL) enables a set of client devices to collaboratively train a model without sharing raw data. This process, though, operates under the constrained computation and communication resources of edge devices. These constraints combined with ...

Institute of Electrical and Electronics Engineers (IEEE)2025

GoldFinger: Fast & Approximate Jaccard for Efficient KNN Graph Constructions

Rachid Guerraoui, Anne-Marie Kermarrec

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 ...

Ieee Computer Soc2023

Smaller, Faster & Lighter KNN Graph Constructions

Rachid Guerraoui, Anne-Marie Kermarrec

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