# Differentially Private Federated Learning via Inexact ADMM

Source: [Differentially Private Federated Learning via Inexact ADMM](<https://www.sota2.com/research/paper/68fa6aef1e209131cea2b2d5>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:36:16.560Z

Authors: Minseok Ryu, Kibaek Kim

Published: 2021-06-11T02:28:07.000Z

## Abstract

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 $\\bar{\\epsilon}$-DP for every iteration, where $\\bar{\\epsilon}$ 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2106.06127>)

[PDF](<https://arxiv.org/pdf/2106.06127.pdf>)