Anne-Marie Kermarrec

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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 ...

Large language models (LLMs) have demonstrated remarkable capabilities across various domains but remain susceptible to hallucinations and inconsistencies, limiting their reliability. Retrieval-augmented generation (RAG) mitigates these issues by grounding ...

Gossip protocols are considered very effective to disseminate information in a large scale dynamic distributed system. Their inherent simplicity makes them easy to implement and deploy. However, whereas their probabilistic guarantees are often enough to di ...

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 ...

Federated learning (FL) is a machine learning approach where nodes collaboratively train a global model. As more nodes participate in a round of FL, the effectiveness of individual model updates by nodes also diminishes. In this study, we increase the effe ...

The advent of online social networks created new prediction opportunities for recommender systems: instead of relying on past rating history through the use of collaborative filtering (CF), they can leverage the social relations among users as a predictor ...

Recommenders are central in many applications today. The most effective recommendation schemes, such as those based on collaborative filtering (CF), exploit similarities between user profiles to make recommendations, but potentially expose private data. Fe ...

Peer-to-peer (P2P) architectures are popular for tasks such as collaborative download, VoIP telephony, and backup. To maximize performance in the face of widely variable storage capacities and bandwidths, such systems typically need to shift work from poor ...

Gossip-based communication protocols are appealing in large-scale distributed applications such as information dissemination, aggregation, and overlay topology management. This paper factors out a fundamental mechanism at the heart of all these protocols: ...

Decentralized learning (DL) is an emerging technique that allows nodes on the web to collaboratively train machine learning models without sharing raw data. Dealing with stragglers, i.e., nodes with slower compute or communication than others, is a key cha ...

This article presents P4Q, a fully decentralized gossip-based protocol to personalize query processing in social tagging systems. P4Q dynamically associates each user with social acquaintances sharing similar tagging behaviors. Queries are gossiped among s ...

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 ...

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 ...

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 ...

Existing work in fairness auditing assumes that each audit is performed independently. In this paper, we consider multiple agents working together, each auditing the same platform for different tasks. Agents have two levers: their collaboration strategy, w ...

Decentralized learning (DL) is an emerging paradigm of collaborative machine learning that enables nodes in a network to train models collectively without sharing their raw data or relying on a central server. This paper introduces Zip-DL, a privacy-aware ...

Gossip-based information dissemination protocols are considered easy to deploy, scalable and resilient to network dynamics. Load-balancing is inherent in these protocols as the dissemination work is evenly spread among all nodes. Yet, large-scale distribut ...

Decentralized learning (DL) systems have been gaining popularity because they avoid raw data sharing by communicating only model parameters, hence preserving data confidentiality. However, the large size of deep neural networks poses a significant challeng ...

The notion of adversary is a staple of distributed computing. An adversary typically models “hostile” assumptions about the underlying distributed environment, e.g., a network that can drop messages, an operating system that can delay processes or an attac ...

SKIPTRAIN is a novel Decentralized Learning (DL) algorithm, which minimizes energy consumption in decentralized learning by strategically skipping some training rounds and substituting them with synchronization rounds. These trainingsilent periods, besides ...

Anne-Marie Kermarrec

Chat with Graph Search

Fingerprinting Big Data: The Case of KNN Graph Construction

Efficient Federated Search for Retrieval-Augmented Generation

Boosting Gossip for Live Streaming

Privacy-preserving distributed collaborative filtering

Harnessing Increased Client Participation with Cohort-Parallel Federated Learning

Comparing the Predictive Capability of Social and Interest Affinity for Recommendations

TEE-based decentralized recommender systems: The raw data sharing redemption

Slicing Distributed Systems

Gossip-Based Peer Sampling

Boosting Asynchronous Decentralized Learning with Model Fragmentation

Collaborative Personalized Top-k Processing

ProteusTM: Abstraction Meets Performance in Transactional Memory

D2P: Distance-Based Differential Privacy in Recommenders

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

Fairness Auditing with Multi-Agent Collaboration

Low-Cost Privacy-Preserving Decentralized Learning

Heterogeneous Gossip

Get More for Less in Decentralized Learning Systems

The Universal Gossip Fighter

Energy-Aware Decentralized Learning with Intermittent Model Training