Publications

2025

1. PETS

   Low-Cost Privacy-Preserving Decentralized Learning

   Sayan Biswas, Davide Frey, Romaric Gaudel, Anne-Marie Kermarrec, Dimitri Lerévérend, Rafael Pires, Rishi Sharma, and François Taïani

   Proceedings on Privacy Enhancing Technologies, 2025

   Abs DOI Bib HTML

   This paper introduces ZIP-DL, a novel privacy-aware decentralized learning (DL) algorithm that relies on adding correlated noise to each model update during the model training process. This technique ensures that the added noise almost neutralizes itself during the aggregation process due to its correlation, thus minimizing the impact on model accuracy. In addition, ZIP-DL does not require multiple communication rounds for noise cancellation, addressing the common trade-off between privacy protection and communication overhead. We provide theoretical guarantees for both convergence speed and privacy guarantees, thereby making ZIP-DL applicable to practical scenarios. Our extensive experimental study shows that ZIP-DL achieves the best trade-off between vulnerability and accuracy. In particular, ZIP-DL (i) reduces the effectiveness of a linkability attack by up to 52 points compared to baseline DL, and (ii) achieves up to 37 more accuracy points for the same vulnerability under membership inference attacks against a privacy-preserving competitor

   @article{biswasLowCostPrivacyPreservingDecentralized2025,
     title = {Low-{{Cost Privacy-Preserving Decentralized Learning}}},
     author = {Biswas, Sayan and Frey, Davide and Gaudel, Romaric and Kermarrec, Anne-Marie and Ler{\'e}v{\'e}rend, Dimitri and Pires, Rafael and Sharma, Rishi and Ta{\"i}ani, Fran{\c c}ois},
     year = {2025},
     journal = {Proceedings on Privacy Enhancing Technologies},
     volume = {2025},
     number = {3},
     pages = {451--474},
     issn = {2299-0984},
     doi = {10.56553/popets-2025-0108},
     urldate = {2025-05-20},
     url = {https://petsymposium.org/popets/2025/popets-2025-0108.php},
     keywords = {Decentralized learning, Differential privacy, Correlated noises},
   }