94 citations · 204 across the 8 of their papers we have counts for
8 papers
Count-Min Sketch with Conservative Updates: Worst-Case Analysis
Younes Ben Mazziane, Othmane Marfoq
Count-Min Sketch with Conservative Updates (CMS-CU) is a memory-efficient hash-based data structure used to estimate the occurrences of items within a data stream. CMS-CU stores $m…
A Cautionary Tale: On the Role of Reference Data in Empirical Privacy Defenses
Caelin G. Kaplan, Chuan Xu, Othmane Marfoq +2
Within the realm of privacy-preserving machine learning, empirical privacy defenses have been proposed as a solution to achieve satisfactory levels of training data privacy without…
Federated Learning under Heterogeneous and Correlated Client Availability
Angelo Rodio, Francescomaria Faticanti, Othmane Marfoq +2
The enormous amount of data produced by mobile and IoT devices has motivated the development of federated learning (FL), a framework allowing such devices (or clients) to collabora…
Federated Learning for Data Streams
Othmane Marfoq, Giovanni Neglia, Laetitia Kameni +1
Federated learning (FL) is an effective solution to train machine learning models on the increasing amount of data generated by IoT devices and smartphones while keeping such data…
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers +21
Federated Learning (FL) is a novel approach enabling several clients holding sensitive data to collaboratively train machine learning models, without centralizing data. The cross-s…
Personalized Federated Learning through Local Memorization
Othmane Marfoq, Giovanni Neglia, Laetitia Kameni +1
Federated learning allows clients to collaboratively learn statistical models while keeping their data local. Federated learning was originally used to train a unique global model…
Federated Multi-Task Learning under a Mixture of Distributions
Othmane Marfoq, Giovanni Neglia, Aurélien Bellet +2
The increasing size of data generated by smartphones and IoT devices motivated the development of Federated Learning (FL), a framework for on-device collaborative training of machi…
Throughput-Optimal Topology Design for Cross-Silo Federated Learning
Othmane Marfoq, Chuan Xu, Giovanni Neglia +1
Federated learning usually employs a client-server architecture where an orchestrator iteratively aggregates model updates from remote clients and pushes them back a refined model.…