mlsanityData to 14 Oct 2026

Differentially Private Federated Clustering over Non-IID Data

arXiv 2301.00955 ↗First posted 3 Jan 2023cs.DC cs.LGcited by 7

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Abstract (arXiv)

In this paper, we investigate federated clustering (FedC) problem, that aims to accurately partition unlabeled data samples distributed over massive clients into finite clusters under the orchestration of a parameter server, meanwhile considering data privacy. Though it is an NP-hard optimization problem involving real variables denoting cluster centroids and binary variables denoting the cluster membership of each data sample, we judiciously reformulate the FedC problem into a non-convex optimization problem with only one convex constraint, accordingly yielding a soft clustering solution. Then a novel FedC algorithm using differential privacy (DP) technique, referred to as DP-FedC, is proposed in which partial clients participation and multiple local model updating steps are also considered. Furthermore, various attributes of the proposed DP-FedC are obtained through theoretical analyses of privacy protection and convergence rate, especially for the case of non-identically and independently distributed (non-i.i.d.) data, that ideally serve as the guidelines for the design of the proposed DP-FedC. Then some experimental results on two real datasets are provided to demonstrate the efficacy of the proposed DP-FedC together with its much superior performance over some state-of-the-art FedC algorithms, and the consistency with all the presented analytical results.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 28 of its 57 references that are in the map. First posted on arXiv 3 Jan 2023.

Builds on

28 of 57 references.

References · 28

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  3. 2016Deep Learning with Differential Privacycited by 2,728
  4. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  5. 2018How To Backdoor Federated Learningcited by 765
  6. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 712
  7. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 564
  8. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 503
  9. 2018Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learningcited by 278
  10. 2018Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergencescited by 242
  11. 2015Federated Optimization:Distributed Optimization Beyond the Datacentercited by 195
  12. 2018Amplification by Shuffling: From Local to Central Differential Privacy via Anonymitycited by 164
16 more
  1. 2020FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Datacited by 144
  2. 2019Secure Federated Matrix Factorizationcited by 102
  3. 2021Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learningcited by 93
  4. 2021Asynchronous Federated Learning on Heterogeneous Devices: A Surveycited by 93
  5. 2019Robust Federated Learning in a Heterogeneous Environmentcited by 90
  6. 2021Heterogeneity for the Win: One-Shot Federated Clusteringcited by 87
  7. 2022On the Convergence of Clustered Federated Learningcited by 31
  8. 2022Towards Federated Clustering: A Federated Fuzzy c-Means Algorithm (FFCM)cited by 30
  9. 2013Distributed k-Means and k-Median Clustering on General Topologiescited by 24
  10. 2021Gradient Leakage Attack Resilient Deep Learningcited by 18
  11. 2022Secure Federated Clusteringcited by 10
  12. 2019Clustering by Orthogonal NMF Model and Non-Convex Penalty Optimizationcited by 9
  13. 2021Private Hierarchical Clustering in Federated Networkscited by 8
  14. 2021Federated Semi-Supervised Learning with Class Distribution Mismatchcited by 6
  15. 2016Learning From Hidden Traits: Joint Factor Analysis and Latent Clusteringcited by 5
  16. 2022Federated Stochastic Primal-dual Learning with Differential Privacycited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 3; 2026: 2.

7 papers.

  1. 2023Privacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsificationcited by 2
  2. 2024Jigsaw Game: Federated Clusteringcited by 1
  3. 2025Communication-Efficient Personalized Distributed Learning with Data and Node Heterogeneitycited by 1
  4. 2025Differentially Private Federated Clustering with Random Rebalancingcited by 0
  5. 2025Federated Learning with Enhanced Privacy via Model Splitting and Random Client Participationcited by 0
  6. 2026One-Shot Federated Clustering of Non-Independent Completely Distributed Datacited by 0
  7. 2026Federated Hierarchical Clustering with Automatic Selection of Optimal Cluster Numberscited by 0