Pedro Miguel Sanchez Sanchez

This paper introduces S-VOTE, a similarity-based voting mechanism designed to improve both efficiency and model performance in Decentralized Federated Learning (DFL). Unlike traditional federated learning, DFL operates without a central server, relying on peer-to-peer communication. While this avoids bottlenecks and… Read More »S-VOTE: Similarity-based Voting for Client Selection in Decentralized Federated Learning

This paper introduces ProFe, a new algorithm designed to make Decentralized Federated Learning (DFL) more communication-efficient without compromising model performance. In DFL, clients collaborate without a central server, which avoids single-point failures but creates significant communication overhead—especially when nodes have… Read More »ProFe: Communication-Efficient Decentralized Federated Learning via Distillation and Prototypes