# QMGeo: Differentially Private Federated Learning via Stochastic Quantization with Mixed Truncated Geometric Distribution

arXiv: https://arxiv.org/abs/2312.05761 · HTML: https://mlsanity.com/paper/2312.05761/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 21 references that are in the map.
- First posted on arXiv 10 Dec 2023.

## Builds on (most cited first, 14 of 14)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programming](https://mlsanity.com/paper/1309.5549/) (2013)
- [RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response](https://mlsanity.com/paper/1407.6981/) (2014)
- [Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences](https://mlsanity.com/paper/1807.01647/) (2018)
- [Subsampled R'enyi Differential Privacy and Analytical Moments Accountant](https://mlsanity.com/paper/1808.00087/) (2018)
- [cpSGD: Communication-efficient and differentially-private distributed SGD](https://mlsanity.com/paper/1805.10559/) (2018)
- [Collecting Telemetry Data Privately](https://mlsanity.com/paper/1712.01524/) (2017)
- [The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation](https://mlsanity.com/paper/2102.06387/) (2021)
- [UVeQFed: Universal Vector Quantization for Federated Learning](https://mlsanity.com/paper/2006.03262/) (2020)
- [Federated Learning With Quantized Global Model Updates](https://mlsanity.com/paper/2006.10672/) (2020)
- [Towards Demystifying Membership Inference Attacks](https://mlsanity.com/paper/1807.09173/) (2018)
- [Quantization enabled Privacy Protection in Decentralized Stochastic Optimization](https://mlsanity.com/paper/2208.04845/) (2022)

## Built on by (most cited first, 1 of 1)

- [Privacy-Preserving Quantized Federated Learning with Diverse Precision](https://mlsanity.com/paper/2507.00920/) (2025)

## Abstract (arXiv)

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.