# An Adaptive Differential Privacy Method Based on Federated Learning

arXiv: https://arxiv.org/abs/2408.08909 · HTML: https://mlsanity.com/paper/2408.08909/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 13 Aug 2024.

## Builds on (most cited first, 5 of 5)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Differentially Private Federated Learning: A Client Level Perspective](https://mlsanity.com/paper/1712.07557/) (2017)
- [Federated Learning with Bayesian Differential Privacy](https://mlsanity.com/paper/1911.10071/) (2019)
- [An Accuracy-Lossless Perturbation Method for Defending Privacy Attacks in Federated Learning](https://mlsanity.com/paper/2002.09843/) (2020)

## Built on by (most cited first, 3 of 3)

- [PLM: Efficient Peripheral Language Models Hardware-Co-Designed for Ubiquitous Computing](https://mlsanity.com/paper/2503.12167/) (2025)
- [Fast Fourier Transform-Based Spectral and Temporal Gradient Filtering for Differential Privacy](https://mlsanity.com/paper/2505.04468/) (2025)
- [Evaluation of Differential Privacy Mechanisms on Federated Learning](https://mlsanity.com/paper/2510.09691/) (2025)

## Abstract (arXiv)

Differential privacy is one of the methods to solve the problem of privacy protection in federated learning. Setting the same privacy budget for each round will result in reduced accuracy in training. The existing methods of the adjustment of privacy budget consider fewer influencing factors and tend to ignore the boundaries, resulting in unreasonable privacy budgets. Therefore, we proposed an adaptive differential privacy method based on federated learning. The method sets the adjustment coefficient and scoring function according to accuracy, loss, training rounds, and the number of datasets and clients. And the privacy budget is adjusted based on them. Then the local model update is processed according to the scaling factor and the noise. Fi-nally, the server aggregates the noised local model update and distributes the noised global model. The range of parameters and the privacy of the method are analyzed. Through the experimental evaluation, it can reduce the privacy budget by about 16%, while the accuracy remains roughly the same.