mlsanityData to 7 Oct 2026

An Optimal Transport Approach to Personalized Federated Learning

arXiv 2206.02468 ↗First posted 6 Jun 2022cs.LG cs.AI stat.MLcited by 12

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Abstract (arXiv)

Federated learning is a distributed machine learning paradigm, which aims to train a model using the local data of many distributed clients. A key challenge in federated learning is that the data samples across the clients may not be identically distributed. To address this challenge, personalized federated learning with the goal of tailoring the learned model to the data distribution of every individual client has been proposed. In this paper, we focus on this problem and propose a novel personalized Federated Learning scheme based on Optimal Transport (FedOT) as a learning algorithm that learns the optimal transport maps for transferring data points to a common distribution as well as the prediction model under the applied transport map. To formulate the FedOT problem, we extend the standard optimal transport task between two probability distributions to multi-marginal optimal transport problems with the goal of transporting samples from multiple distributions to a common probability domain. We then leverage the results on multi-marginal optimal transport problems to formulate FedOT as a min-max optimization problem and analyze its generalization and optimization properties. We discuss the results of several numerical experiments to evaluate the performance of FedOT under heterogeneous data distributions in federated learning problems.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 41 of its 57 references that are in the map. First posted on arXiv 6 Jun 2022.

Builds on

41 of 57 references.

References · 41

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  2. 2014Going Deeper with Convolutionscited by 4,231
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2019Invariant Risk Minimizationcited by 1,832
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  10. 2016TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systemscited by 660
  11. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  12. 2021Exploiting Shared Representations for Personalized Federated Learningcited by 537
29 more
  1. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  2. 2019Agnostic Federated Learningcited by 451
  3. 2020Adaptive Personalized Federated Learningcited by 353
  4. 2020Think Locally, Act Globally: Federated Learning with Local and Global Representationscited by 322
  5. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
  6. 2019On Gradient Descent Ascent for Nonconvex-Concave Minimax Problemscited by 266
  7. 2019Improving Federated Learning Personalization via Model Agnostic Meta Learningcited by 264
  8. 2019Model Fusion via Optimal Transportcited by 217
  9. 2013Fast Computation of Wasserstein Barycenterscited by 216
  10. 2021Personalized Federated Learning using Hypernetworkscited by 210
  11. 2020Federated Learning of a Mixture of Global and Local Modelscited by 208
  12. 2018Statistical Aspects of Wasserstein Distancescited by 202
  13. 2019SCAFFOLD: Stochastic Controlled Averaging for Federated Learningcited by 174
  14. 2019Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learningcited by 169
  15. 2019Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methodscited by 148
  16. 2021Multi-Center Federated Learning: Clients Clustering for Better Personalizationcited by 112
  17. 2021Distributionally Robust Federated Averagingcited by 104
  18. 2020Robust Federated Learning: The Case of Affine Distribution Shiftscited by 102
  19. 2019Robust Federated Learning in a Heterogeneous Environmentcited by 88
  20. 2017Sliced Wasserstein Distance for Learning Gaussian Mixture Modelscited by 59
  21. 2018On the Convergence and Robustness of Training GANs with Regularized Optimal Transportcited by 51
  22. 2014Multi-marginal optimal transport: theory and applicationscited by 41
  23. 2021Local Stochastic Gradient Descent Ascent: Convergence Analysis and Communication Efficiencycited by 40
  24. 2020Global Convergence and Variance-Reduced Optimization for a Class of Nonconvex-Nonconcave Minimax Problemscited by 37
  25. 2019Multi-marginal Wasserstein GANcited by 32
  26. 2018Stochastic Wasserstein Barycenterscited by 25
  27. 2019Normalized Wasserstein Distance for Mixture Distributions with Applications in Adversarial Learning and Domain Adaptationcited by 23
  28. 2017Unsupervised Multi-Domain Image Translation with Domain-Specific Encoders/Decoderscited by 11
  29. 2021A Wasserstein Minimax Framework for Mixed Linear Regressioncited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 4; 2025: 4; 2026: 2.

12 papers.

  1. 2024Global and Local Prompts Cooperation via Optimal Transport for Federated Learningcited by 45
  2. 2023Federated Domain Generalization: A Surveycited by 25
  3. 2024Robust Decentralized Learning with Local Updates and Gradient Trackingcited by 8
  4. 2022PersA-FL: Personalized Asynchronous Federated Learningcited by 5
  5. 2025pFedMMA: Personalized Federated Fine-Tuning with Multi-Modal Adapter for Vision-Language Modelscited by 3
  6. 2026Distribution Alignment for One-Shot Federated Learning via Optimal Transportcited by 1
  7. 2024Certifiably Robust Model Evaluation in Federated Learning under Meta-Distributional Shiftscited by 0
  8. 2024Federated Learning for Discrete Optimal Transport with Large Population under Incomplete Informationcited by 0
  9. 2025Optimal Transport-based Domain Alignment as a Preprocessing Step for Federated Learningcited by 0
  10. 2025Cost-Free Personalization via Information-Geometric Projection in Bayesian Federated Learningcited by 0
  11. 2025Data Valuation and Selection in a Federated Model Marketplacecited by 0
  12. 2026Collaborative and Efficient Fine-tuning: Leveraging Task Similaritycited by 0