# Communication Efficient Private Federated Learning Using Dithering

arXiv: https://arxiv.org/abs/2309.07809 · HTML: https://mlsanity.com/paper/2309.07809/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 27 references that are in the map.
- First posted on arXiv 14 Sep 2023.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks](https://mlsanity.com/paper/1802.08232/) (2018)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [Opacus: User-Friendly Differential Privacy Library in PyTorch](https://mlsanity.com/paper/2109.12298/) (2021)
- [Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences](https://mlsanity.com/paper/1807.01647/) (2018)
- [Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising](https://mlsanity.com/paper/1805.06530/) (2018)
- [R'enyi Differential Privacy of the Sampled Gaussian Mechanism](https://mlsanity.com/paper/1908.10530/) (2019)
- [Federated Learning with Buffered Asynchronous Aggregation](https://mlsanity.com/paper/2106.06639/) (2021)
- [Distributed Mean Estimation with Limited Communication](https://mlsanity.com/paper/1611.00429/) (2016)
- [Reconstructing Training Data from Trained Neural Networks](https://mlsanity.com/paper/2206.07758/) (2022)
- [PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments](https://mlsanity.com/paper/2104.14380/) (2021)
- [Natural Compression for Distributed Deep Learning](https://mlsanity.com/paper/1905.10988/) (2019)
- [Joint Privacy Enhancement and Quantization in Federated Learning](https://mlsanity.com/paper/2208.10888/) (2022)
- [Optimal Compression of Locally Differentially Private Mechanisms](https://mlsanity.com/paper/2111.00092/) (2021)
- [Privacy-Aware Compression for Federated Data Analysis](https://mlsanity.com/paper/2203.08134/) (2022)
- [Fast Optimal Locally Private Mean Estimation via Random Projections](https://mlsanity.com/paper/2306.04444/) (2023)
- [DP-REC: Private & Communication-Efficient Federated Learning](https://mlsanity.com/paper/2111.05454/) (2021)
- [Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation](https://mlsanity.com/paper/2306.04924/) (2023)

## Built on by (most cited first, 9 of 9)

- [Compression with Exact Error Distribution for Federated Learning](https://mlsanity.com/paper/2310.20682/) (2023)
- [Universal Exact Compression of Differentially Private Mechanisms](https://mlsanity.com/paper/2405.20782/) (2024)
- [The Rate-Distortion-Perception Trade-off: The Role of Private Randomness](https://mlsanity.com/paper/2404.01111/) (2024)
- [Rejection-Sampled Universal Quantization for Smaller Quantization Errors](https://mlsanity.com/paper/2402.03030/) (2024)
- [Communication-Efficient and Privacy-Adaptable Mechanism for Federated Learning](https://mlsanity.com/paper/2501.12046/) (2025)
- [Communication-Efficient and Privacy-Adaptable Mechanism -- a Federated Learning Scheme with Convergence Analysis](https://mlsanity.com/paper/2601.10701/) (2026)
- [Federated learning over physical channels: adaptive algorithms with near-optimal guarantees](https://mlsanity.com/paper/2509.02538/) (2025)
- [Scalable Discrete-to-Continuous Channel Simulation for Compression and Privacy](https://mlsanity.com/paper/2609.12067/) (2026)
- [Privacy-Aligned Personalized Federated Learning with Compact Adaptation and Variable-Length Gaussian Communication](https://mlsanity.com/paper/2609.15950/) (2026)

## Abstract (arXiv)

The task of preserving privacy while ensuring efficient communication is a fundamental challenge in federated learning. In this work, we tackle this challenge in the trusted aggregator model, and propose a solution that achieves both objectives simultaneously. We show that employing a quantization scheme based on subtractive dithering at the clients can effectively replicate the normal noise addition process at the aggregator. This implies that we can guarantee the same level of differential privacy against other clients while substantially reducing the amount of communication required, as opposed to transmitting full precision gradients and using central noise addition. We also experimentally demonstrate that the accuracy of our proposed approach matches that of the full precision gradient method.