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Author

Lorenzo Sani

2 papers indexed here

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Preprint Aug 2026

Flower Hub: A Reproducible Benchmarking Platform for Federated Learning in Simulation and Deployment

Flower Hub is presented, a platform for publishing, discovering, and executing decentralized and federated applications that enables reproducible benchmarking by packaging benchmarks as executable, versioned applications with standardized metadata, pinned dependencies, and explicit evaluation workflows.

Yan Gao, Mohammad Naseri, Javier Fernandez-marques et al. · 0 citations
Conference Open access Sep 2026

Sparsity in Federated Learning: A Survey

A novel taxonomy of sparse FL methods is introduced that systematically organizes the existing literature according to their core objectives and methodological choices, and identifies open challenges, expose research gaps, and extract guidance to help practitioners understand and adopt sparsity mechanisms in FL.

Alessio Mora, Adriano Guastella, Lorenzo Sani et al. · 0 citations

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