mlsanityData to 14 Oct 2026

FedADMM: A Federated Primal-Dual Algorithm Allowing Partial Participation

arXiv 2203.15104 ↗First posted 28 Mar 2022cs.LG cs.SY eess.SYcited by 36

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Abstract (arXiv)

Federated learning is a framework for distributed optimization that places emphasis on communication efficiency. In particular, it follows a client-server broadcast model and is particularly appealing because of its ability to accommodate heterogeneity in client compute and storage resources, non-i.i.d. data assumptions, and data privacy. Our contribution is to offer a new federated learning algorithm, FedADMM, for solving non-convex composite optimization problems with non-smooth regularizers. We prove converges of FedADMM for the case when not all clients are able to participate in a given communication round under a very general sampling model.

Key facts. Cited by 36 papers in the map (data through 14 Oct 2026). Builds on 27 of its 43 references that are in the map. First posted on arXiv 28 Mar 2022.

Builds on

27 of 43 references.

References · 27

  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. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  4. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  5. 2018Federated Learning with Non-IID Datacited by 1,108
  6. 2018LEAF: A Benchmark for Federated Settingscited by 792
  7. 2018Local SGD Converges Fast and Communicates Littlecited by 605
  8. 2021Federated Learning Based on Dynamic Regularizationcited by 562
  9. 2019Agnostic Federated Learningcited by 456
  10. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 409
  11. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 356
  12. 2018On the Convergence of Stochastic Gradient Descent with Adaptive Stepsizescited by 156
15 more
  1. 2020FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Datacited by 144
  2. 2013Communication Efficient Distributed Optimization using an Approximate Newton-type Methodcited by 132
  3. 2021Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradientscited by 123
  4. 2020FedSplit: An algorithmic framework for fast federated optimizationcited by 108
  5. 2018Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithmscited by 106
  6. 2019First Analysis of Local GD on Heterogeneous Datacited by 81
  7. 2020Federated Composite Optimizationcited by 49
  8. 2021FedDR -- Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimizationcited by 29
  9. 2017Douglas-Rachford splitting and ADMM for nonconvex optimization: tight convergence resultscited by 21
  10. 2014Global convergence of splitting methods for nonconvex composite optimizationcited by 19
  11. 2014Douglas-Rachford splitting for nonconvex optimization with application to nonconvex feasibility problemscited by 15
  12. 2021An automatic system to detect equivalence between iterative algorithmscited by 3
  13. 2014Self Equivalence of the Alternating Direction Method of Multiplierscited by 2
  14. 2019A Control-Theoretic Approach to Analysis and Parameter Selection of Douglas-Rachford Splittingcited by 2
  15. 2021Distributed and Localized Model Predictive Control. Part I: Synthesis and Implementationcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 10; 2024: 11; 2025: 8; 2026: 5.

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  1. 2023FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracycited by 49
  2. 2023Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscapecited by 46
  3. 2023Improving the Model Consistency of Decentralized Federated Learningcited by 45
  4. 2023Federated Temporal Difference Learning with Linear Function Approximation under Environmental Heterogeneitycited by 27
  5. 2023Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initializationcited by 21
  6. 2023Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updatescited by 21
  7. 2022FedSysID: A Federated Approach to Sample-Efficient System Identificationcited by 17
  8. 2023Fusion of Global and Local Knowledge for Personalized Federated Learningcited by 14
  9. 2024Momentum for the Win: Collaborative Federated Reinforcement Learning across Heterogeneous Environmentscited by 8
  10. 2023Composite federated learning with heterogeneous datacited by 7
  11. 2025Collaborative Bayesian Optimization via Wasserstein Barycenterscited by 7
  12. 2023DFedADMM: Dual Constraints Controlled Model Inconsistency for Decentralized Federated Learningcited by 6
24 more
  1. 2025Knowledge Adaptation as Posterior Correctioncited by 6
  2. 2024Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Datacited by 5
  3. 2024FedADMM-InSa: An Inexact and Self-Adaptive ADMM for Federated Learningcited by 4
  4. 2024Asynchronous Heterogeneous Linear Quadratic Regulator Designcited by 4
  5. 2025Connecting Federated ADMM to Bayescited by 4
  6. 2025FedCanon: Non-Convex Composite Federated Learning with Efficient Proximal Operation on Heterogeneous Datacited by 4
  7. 2023Communication-Efficient Heterogeneous Federated Learning with Generalized Heavy-Ball Momentumcited by 3
  8. 2024Distributed Event-Based Learning via ADMMcited by 3
  9. 2025Federated ADMM from Bayesian Dualitycited by 3
  10. 2022FedBC: Calibrating Global and Local Models via Federated Learning Beyond Consensuscited by 2
  11. 2024A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needscited by 2
  12. 2024Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacycited by 1
  13. 2024Controlling Participation in Federated Learning with Feedbackcited by 1
  14. 2025FedAPM: Federated Learning via ADMM with Partial Model Personalizationcited by 1
  15. 2026First-Order Softmax Weighted Switching Gradient Method for Distributed Stochastic Minimax Optimization with Stochastic Constraintscited by 1
  16. 2026On the Gradient Heterogeneity Dynamics of Adversarially Robust Federated Regressioncited by 1
  17. 2024Federated Frank-Wolfe Algorithmcited by 0
  18. 2024Convergent Differential Privacy Analysis for General Federated Learningcited by 0
  19. 2024Cross-Domain Latent Factors Sharing via Implicit Matrix Factorizationcited by 0
  20. 2025Decentralized Nonconvex Composite Federated Learning with Gradient Tracking and Momentumcited by 0
  21. 2025An Asynchronous Decentralised Optimisation Algorithm for Nonconvex Problemscited by 0
  22. 2026Compressed Proximal Federated Learning for Non-Convex Composite Optimization on Heterogeneous Datacited by 0
  23. 2026Rethinking the Personalized Relaxed Initialization in the Federated Learning: Consistency and Generalizationcited by 0
  24. 2026Co-VLA: Consensus-based Federated Training for Vision-Language-Action Modelscited by 0