Communication-Efficient Federated Learning via Regularized Sparse Random Networks

no code implementations • 19 Sep 2023 • Mohamad Mestoukirdi, Omid Esrafilian, David Gesbert, Qianrui Li, Nicolas Gresset

In this setting, a binary mask is optimized instead of the model weights, which are kept fixed.

Federated Learning

User-Centric Federated Learning: Trading off Wireless Resources for Personalization

no code implementations • 25 Apr 2023 • Mohamad Mestoukirdi, Matteo Zecchin, David Gesbert, Qianrui Li

Statistical heterogeneity across clients in a Federated Learning (FL) system increases the algorithm convergence time and reduces the generalization performance, resulting in a large communication overhead in return for a poor model.

Federated Learning Privacy Preserving

UAV-Aided Multi-Community Federated Learning

no code implementations • 4 Jun 2022 • Mohamad Mestoukirdi, Omid Esrafilian, David Gesbert, Qianrui Li

We propose a heuristic metric as a proxy for the training performance of the different tasks.

Federated Learning Scheduling

User-Centric Federated Learning

no code implementations • 19 Oct 2021 • Mohamad Mestoukirdi, Matteo Zecchin, David Gesbert, Qianrui Li, Nicolas Gresset

Data heterogeneity across participating devices poses one of the main challenges in federated learning as it has been shown to greatly hamper its convergence time and generalization capabilities.

Federated Learning