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Rafael Pires

2 accepted papers

2024

Revisiting Ensembling in One-Shot Federated Learning

NeurIPS 2024poster

Federated Learning (FL) is an appealing approach to training machine learning models without sharing raw data. However, standard FL algorithms are iterative and thus induce a significant communication cost. One-Shot FL (OFL) trades the iterative exchange of models between clients and the server wit…

2023

Epidemic Learning: Boosting Decentralized Learning with Randomized Communication

NeurIPS 2023poster

We present Epidemic Learning (EL), a simple yet powerful decentralized learning (DL) algorithm that leverages changing communication topologies to achieve faster model convergence compared to conventional DL approaches. At each round of EL, each node sends its model updates to a random sample of $s$…