mlsanityData to 14 Oct 2026

Coresets for Vertical Federated Learning: Regularized Linear Regression and K-Means Clustering

arXiv 2210.14664 ↗First posted 26 Oct 2022cs.LG cs.CG cs.DCcited by 14

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Abstract (arXiv)

Vertical federated learning (VFL), where data features are stored in multiple parties distributively, is an important area in machine learning. However, the communication complexity for VFL is typically very high. In this paper, we propose a unified framework by constructing coresets in a distributed fashion for communication-efficient VFL. We study two important learning tasks in the VFL setting: regularized linear regression and k-means clustering, and apply our coreset framework to both problems. We theoretically show that using coresets can drastically alleviate the communication complexity, while nearly maintain the solution quality. Numerical experiments are conducted to corroborate our theoretical findings.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 48 of its 85 references that are in the map. First posted on arXiv 26 Oct 2022.

Builds on

48 of 85 references.

References · 48

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Advances and Open Problems in Federated Learningcited by 3,046
  3. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  5. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  6. 2014SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectivescited by 402
  7. 2021A Field Guide to Federated Optimizationcited by 315
  8. 2019Error Feedback Fixes SignSGD and other Gradient Compression Schemescited by 310
  9. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 176
  10. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 176
  11. 2021EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedbackcited by 133
  12. 2020LDP-Fed: Federated Learning with Local Differential Privacycited by 132
36 more
  1. 2021Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradientscited by 123
  2. 2020Feature Inference Attack on Model Predictions in Vertical Federated Learningcited by 106
  3. 2018Turning Big data into tiny data: Constant-size coresets for k-means, PCA and projective clusteringcited by 94
  4. 2020Acceleration for Compressed Gradient Descent in Distributed and Federated Optimizationcited by 90
  5. 2020Local SGD: Unified Theory and New Efficient Methodscited by 88
  6. 2020VAFL: a Method of Vertical Asynchronous Federated Learningcited by 87
  7. 2021MARINA: Faster Non-Convex Distributed Learning with Compressioncited by 78
  8. 2020Local Differential Privacy based Federated Learning for Internet of Thingscited by 75
  9. 2016Coresets and Sketchescited by 63
  10. 2022Vertical Federated Learning: Challenges, Methodologies and Experimentscited by 55
  11. 2019Parallel Distributed Logistic Regression for Vertical Federated Learning without Third-Party Coordinatorcited by 52
  12. 2015Input Sparsity Time Low-Rank Approximation via Ridge Leverage Score Samplingcited by 46
  13. 2022BEER: Fast O(1/T) Rate for Decentralized Nonconvex Optimization with Communication Compressioncited by 41
  14. 2016New Frameworks for Offline and Streaming Coreset Constructionscited by 38
  15. 2017Training Gaussian Mixture Models at Scale via Coresetscited by 38
  16. 2019A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regressioncited by 38
  17. 2021A New Coreset Framework for Clusteringcited by 38
  18. 2022SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compressioncited by 34
  19. 2014Uniform Sampling for Matrix Approximationcited by 31
  20. 2020Coresets for Clustering in Euclidean Spaces: Importance Sampling is Nearly Optimalcited by 31
  21. 2020A Unified Analysis of Stochastic Gradient Methods for Nonconvex Federated Optimizationcited by 28
  22. 2020FederBoost: Private Federated Learning for GBDTcited by 27
  23. 20223PC: Three Point Compressors for Communication-Efficient Distributed Training and a Better Theory for Lazy Aggregationcited by 27
  24. 2013Distributed k-Means and k-Median Clustering on General Topologiescited by 24
  25. 2021CANITA: Faster Rates for Distributed Convex Optimization with Communication Compressioncited by 23
  26. 2022Towards Optimal Lower Bounds for k-median and k-means Coresetscited by 23
  27. 2021FedPAGE: A Fast Local Stochastic Gradient Method for Communication-Efficient Federated Learningcited by 20
  28. 2018varepsilon-Coresets for Clustering (with Outliers) in Doubling Metricscited by 19
  29. 2020Privacy-Preserving Asynchronous Federated Learning Algorithms for Multi-Party Vertically Collaborative Learningcited by 19
  30. 2021Vertical Federated Learning without Revealing Intersection Membershipcited by 19
  31. 2021Faster Rates for Compressed Federated Learning with Client-Variance Reductioncited by 14
  32. 2019The Communication Complexity of Optimizationcited by 10
  33. 2020On Coresets For Regularized Regressioncited by 10
  34. 2020Coresets for Regressions with Panel Datacited by 8
  35. 2019Robust Coreset Construction for Distributed Machine Learningcited by 6
  36. 2021Robust and Fully-Dynamic Coreset for Continuous-and-Bounded Learning (With Outliers) Problemscited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 5; 2026: 6.

14 papers.

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  1. 2022Vertical Federated Learning: Concepts, Advances and Challengescited by 108
  2. 2024Accelerating Heterogeneous Federated Learning with Closed-form Classifierscited by 10
  3. 2023VertiBench: Advancing Feature Distribution Diversity in Vertical Federated Learning Benchmarkscited by 6
  4. 2025From Risk to Resilience: Towards Assessing and Mitigating the Risk of Data Reconstruction Attacks in Federated Learningcited by 3
  5. 2025A new type of federated clustering: A non-model-sharing approachcited by 1
  6. 2025Bayesian Coreset Optimization for Personalized Federated Learningcited by 1
  7. 2026Training-Free Dynamic Upcycling of Expert Language Modelscited by 1
  8. 2025X-VFL: A New Vertical Federated Learning Framework with Cross Completion and Decision Subspace Alignmentcited by 0
  9. 2025Coresets for Clustering Under Stochastic Noisecited by 0
  10. 2026VertCoHiRF: Decentralized Vertical Clustering Beyond k-meanscited by 0
  11. 2026Learning Unknown Interdependencies for Decentralized Root Cause Analysis in Nonlinear Dynamical Systemscited by 0
  12. 2026COREKG: Coreset-Guided Personalized Summarization of Knowledge Graphscited by 0
2 more
  1. 2026IR3DE: A Linear Router for Large Language Modelscited by 0
  2. 2026Beyond Worst-Case Coreset Bounds for k-Clustering via Determinantal Samplingcited by 0