QMGeo: Differentially Private Federated Learning via Stochastic
  Quantization with Mixed Truncated Geometric Distribution
Zixi Wang M. Cenk Gursoy
Federated learning (FL) is a framework which allows multiple users to jointly
train a global machine learning (ML) model by transmitting only model updates
under the coordination of a parameter server, while being able to keep their
datasets local. One key motivation of such distributed frameworks is to provide
privacy guarantees to the users. However, preserving the users' datasets
locally is shown to be not sufficient for privacy. Several differential privacy
(DP) mechanisms have been proposed to provide provable privacy guarantees by
introducing randomness into the framework, and majority of these mechanisms
rely on injecting additive noise. FL frameworks also face the challenge of
communication efficiency, especially as machine learning models grow in
complexity and size. Quantization is a commonly utilized method, reducing the
communication cost by transmitting compressed representation of the underlying
information. Although there have been several studies on DP and quantization in
FL, the potential contribution of the quantization method alone in providing
privacy guarantees has not been extensively analyzed yet. We in this paper
present a novel stochastic quantization method, utilizing a mixed geometric
distribution to introduce the randomness needed to provide DP, without any
additive noise. We provide convergence analysis for our framework and
empirically study its performance.