Anne-Marie Kermarrec

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Decentralized learning (DL) enables collaborative learning without a server and without training data leaving the users' devices. However, the models shared in DL can still be used to infer training data. Conventional defenses such as differential privacy ...

Analyzing gigapixel images is recognized as computationally demanding. In this paper, we introduce PyramidAI, a technique for analyzing gigapixel images with reduced computational cost. The proposed approach adopts a gradual analysis of the image, beginnin ...

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 ...

We present WhatsUp, a collaborative filtering system for disseminating news items in a large-scale dynamic setting with no central authority. WhatsUp constructs an implicit social network based on user profiles that express the opinions of users about the ...

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 ...

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 ...

We present Epidemic Learning ( EL ), a simple yet powerful decentralized learning (DL) algorithm that leverages changing communication topologies to achieve faster model convergence compared to conventional DL approaches. At each round of EL, each node sen ...

We propose a new mechanism to preserve privacy while leveraging user profiles in distributed recommender systems. Our mechanism relies on (i) an original obfuscation scheme to hide the exact profiles of users without significantly decreasing their utility, ...

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 ...

We consider the problem of securely conducting a poll in synchronous dynamic networks equipped with a Public Key Infrastructure (PKI). Whereas previous distributed solutions had a communication cost of O(n^2) in an n nodes system, we present SPP (Secure an ...

Peer-to-peer content dissemination applications suffer immensely from freeriders, i.e., nodes that do not provide their fair share. The Tit-for-Tat (TfT) incentives have received much attention as they help make such systems more robust against freeriding. ...

Federated learning (FL) is an appealing approach to training machine learning models without sharing raw data. However, standard FL algorithms are iterative and thus induce a significant communication cost. One-shot federated learning (OFL) trades the iter ...

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 ...

Gossip-based information dissemination protocols are considered easy to deploy, scalable and resilient to network dynamics. They are also considered highly ﬂexible, namely tunable at will to increase their robustness and adapt to churn. So far however, the ...

In this paper we present an empirical study of a workload gathered by crawling the eDonkey network - a dominant peer-to-peer file sharing system - for over 50 days. We first confirm the presence of some known features, in particular the prevalence of free- ...

We consider the polling problem in a social network: participants express support for a given option and expect an outcome reflecting the opinion of the majority. Individuals in a social network care about their reputation: they do not want their vote to b ...

Anne-Marie Kermarrec

Chattez avec Graph Search

Noiseless Privacy-Preserving Decentralized Learning

Efficient Pyramidal Analysis of Gigapixel Images on a Decentralized Modest Computer Cluster

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

WHATSUP: A Decentralized Instant News Recommender

Cluster-and-Conquer: When Randomness Meets Graph Locality

FeGAN: Scaling Distributed GANs

Epidemic Learning: Boosting Decentralized Learning with Randomized Communication

Privacy-Preserving Distributed Collaborative Filtering

FLEET: Online Federated Learning via Staleness Awareness and Performance Prediction

Scalable and Secure Polling in Dynamic Distributed Networks

On Tracking Freeriders in Gossip Protocols

Distributed Churn Measurement for Arbitrary Networks

Revisiting Ensembling in One-Shot Federated Learning

The Fake News Vaccine

Stretching Gossip with Live Streaming

The Utility and Privacy Effects of a Click

Brief Announcement: Towards Secured Distributed Polling in Social Networks

Peer Sharing Behaviour in the eDonkey Network, and Implications for the Design of Server-less File Sharing Systems

Decentralized polling with respectable participants

A Fast Distributed Slicing Algorithm