mlsanityData to 7 Oct 2026

Differentially Private Federated Learning via Inexact ADMM

arXiv 2106.06127 ↗First posted 11 Jun 2021cs.LGcited by 11

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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.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 17 of its 36 references that are in the map. First posted on arXiv 11 Jun 2021.

Builds on

17 of 36 references.

References · 17

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2016Deep Learning with Differential Privacycited by 2,682
  6. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  7. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  8. 2018LEAF: A Benchmark for Federated Settingscited by 780
  9. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  10. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
  11. 2015Federated Optimization:Distributed Optimization Beyond the Datacentercited by 193
  12. 2018cpSGD: Communication-efficient and differentially-private distributed SGDcited by 157
5 more
  1. 2017Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysiscited by 145
  2. 2018DP-ADMM: ADMM-based Distributed Learning with Differential Privacycited by 41
  3. 2019GADMM: Fast and Communication Efficient Framework for Distributed Machine Learningcited by 30
  4. 2017Differentially Private Empirical Risk Minimization with Input Perturbationcited by 19
  5. 2020Input Perturbation: A New Paradigm between Central and Local Differential Privacycited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 4; 2023: 3; 2024: 1; 2025: 1; 2026: 1.

11 papers.

  1. 2022Federated Learning via Inexact ADMMcited by 44
  2. 2022APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learningcited by 20
  3. 2021Communication-Efficient ADMM-based Federated Learningcited by 14
  4. 2022FedGiA: An Efficient Hybrid Algorithm for Federated Learningcited by 8
  5. 2023From Noisy Fixed-Point Iterations to Private ADMM for Centralized and Federated Learningcited by 5
  6. 2023Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMMcited by 3
  7. 2023Sparse Decentralized Federated Learningcited by 2
  8. 2024Data Collaboration Analysis with Orthonormal Basis Selection and Alignmentcited by 2
  9. 2022Exact Penalty Method for Federated Learningcited by 1
  10. 2025Communication-Efficient Distributed Asynchronous ADMMcited by 0
  11. 2026Scalable and Private Federated Learning Using Distributed Differential Privacy and Secure Aggregationcited by 0