# Differentially Private Federated Learning via Inexact ADMM

arXiv: https://arxiv.org/abs/2106.06127 · HTML: https://mlsanity.com/paper/2106.06127/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 36 references that are in the map.
- First posted on arXiv 11 Jun 2021.

## Builds on (most cited first, 17 of 17)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection](https://mlsanity.com/paper/1907.09693/) (2019)
- [Federated Optimization:Distributed Optimization Beyond the Datacenter](https://mlsanity.com/paper/1511.03575/) (2015)
- [cpSGD: Communication-efficient and differentially-private distributed SGD](https://mlsanity.com/paper/1805.10559/) (2018)
- [Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis](https://mlsanity.com/paper/1706.03446/) (2017)
- [DP-ADMM: ADMM-based Distributed Learning with Differential Privacy](https://mlsanity.com/paper/1808.10101/) (2018)
- [GADMM: Fast and Communication Efficient Framework for Distributed Machine Learning](https://mlsanity.com/paper/1909.00047/) (2019)
- [Differentially Private Empirical Risk Minimization with Input Perturbation](https://mlsanity.com/paper/1710.07425/) (2017)
- [Input Perturbation: A New Paradigm between Central and Local Differential Privacy](https://mlsanity.com/paper/2002.08570/) (2020)

## Built on by (most cited first, 11 of 11)

- [Federated Learning via Inexact ADMM](https://mlsanity.com/paper/2204.10607/) (2022)
- [APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning](https://mlsanity.com/paper/2202.03672/) (2022)
- [Communication-Efficient ADMM-based Federated Learning](https://mlsanity.com/paper/2110.15318/) (2021)
- [FedGiA: An Efficient Hybrid Algorithm for Federated Learning](https://mlsanity.com/paper/2205.01438/) (2022)
- [From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learning](https://mlsanity.com/paper/2302.12559/) (2023)
- [Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM](https://mlsanity.com/paper/2304.12534/) (2023)
- [Sparse Decentralized Federated Learning](https://mlsanity.com/paper/2308.16671/) (2023)
- [Data Collaboration Analysis with Orthonormal Basis Selection and Alignment](https://mlsanity.com/paper/2403.02780/) (2024)
- [Exact Penalty Method for Federated Learning](https://mlsanity.com/paper/2208.11231/) (2022)
- [Communication-Efficient Distributed Asynchronous ADMM](https://mlsanity.com/paper/2508.12233/) (2025)
- [Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregation](https://mlsanity.com/paper/2604.07125/) (2026)

## Abstract (arXiv)

Differential privacy (DP) techniques can be applied to the federated learning model to protect data privacy against inference attacks to communication among the learning agents. The DP techniques, however, hinder achieving a greater learning performance while ensuring strong data privacy. In this paper we develop a DP inexact alternating direction method of multipliers algorithm that solves a sequence of subproblems with the objective perturbation by random noises generated from a Laplace distribution. We show that our algorithm provides barε-DP for every iteration, where barε is a privacy parameter controlled by a user. Using MNIST and FEMNIST datasets for the image classification, we demonstrate that our algorithm reduces the testing error by at most 22% compared with the existing DP algorithm, while achieving the same level of data privacy. The numerical experiment also shows that our algorithm converges faster than the existing algorithm.