50 citations · 97 across the 2 of their papers we have counts for
3 papers
cs.LG2021★ 47 cited
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learning
Yann Fraboni, Richard Vidal, Laetitia Kameni +1
This work addresses the problem of optimizing communications between server and clients in federated learning (FL). Current sampling approaches in FL are either biased, or non opti…
cs.LG2020★ 50 cited
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.…
cs.LG2020
Free-rider Attacks on Model Aggregation in Federated Learning
Yann Fraboni, Richard Vidal, Marco Lorenzi
Free-rider attacks against federated learning consist in dissimulating participation to the federated learning process with the goal of obtaining the final aggregated model without…