Paper accepted at PoPETS 2025

Congratulations to Dimitri Lereverend, Davide Frey, and François Taiani for their work “Low-Cost Privacy-Preserving Decentralized Learning” accepted at PoPETS 2025. The paper explores the use of correlated noise in decentralized learning to achieve an efficient privacy-utility tradeoff. Decentralized learning allows machine learning while keeping individual data local and private, but…