# Voting-based Approaches For Differentially Private Federated Learning

arXiv: https://arxiv.org/abs/2010.04851 · HTML: https://mlsanity.com/paper/2010.04851/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Oct 2020.

## Built on by (most cited first, 14 of 14)

- [Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges](https://mlsanity.com/paper/2201.08135/) (2022)
- [Differentially Private Federated Learning with Local Regularization and Sparsification](https://mlsanity.com/paper/2203.03106/) (2022)
- [Make Landscape Flatter in Differentially Private Federated Learning](https://mlsanity.com/paper/2303.11242/) (2023)
- [A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning](https://mlsanity.com/paper/2206.04621/) (2022)
- [A Survey of What to Share in Federated Learning: Perspectives on Model Utility, Privacy Leakage, and Communication Efficiency](https://mlsanity.com/paper/2307.10655/) (2023)
- [Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks](https://mlsanity.com/paper/2209.04030/) (2022)
- [Constrained Differentially Private Federated Learning for Low-bandwidth Devices](https://mlsanity.com/paper/2103.00342/) (2021)
- [Communication and Energy Efficient Wireless Federated Learning with Intrinsic Privacy](https://mlsanity.com/paper/2304.07460/) (2023)
- [Towards the Flatter Landscape and Better Generalization in Federated Learning under Client-level Differential Privacy](https://mlsanity.com/paper/2305.00873/) (2023)
- [Federated Learning for Smart Grid: A Survey on Applications and Potential Vulnerabilities](https://mlsanity.com/paper/2409.10764/) (2024)
- [DP2-FedSAM: Enhancing Differentially Private Federated Learning Through Personalized Sharpness-Aware Minimization](https://mlsanity.com/paper/2409.13645/) (2024)
- [Differential Privacy Personalized Federated Learning Based on Dynamically Sparsified Client Updates](https://mlsanity.com/paper/2503.09192/) (2025)
- [DP-Hype: Federated Differentially Private Hyperparameter Search](https://mlsanity.com/paper/2510.04902/) (2025)
- [Tackling Privacy Heterogeneity in Differentially Private Federated Learning](https://mlsanity.com/paper/2602.22633/) (2026)

## Abstract (arXiv)

Differentially Private Federated Learning (DPFL) is an emerging field with many applications. Gradient averaging based DPFL methods require costly communication rounds and hardly work with large-capacity models, due to the explicit dimension dependence in its added noise. In this work, inspired by knowledge transfer non-federated privacy learning from Papernot et al.(2017; 2018), we design two new DPFL schemes, by voting among the data labels returned from each local model, instead of averaging the gradients, which avoids the dimension dependence and significantly reduces the communication cost. Theoretically, by applying secure multi-party computation, we could exponentially amplify the (data-dependent) privacy guarantees when the margin of the voting scores are large. Extensive experiments show that our approaches significantly improve the privacy-utility trade-off over the state-of-the-arts in DPFL.