# FedADMM: A Federated Primal-Dual Algorithm Allowing Partial Participation

arXiv: https://arxiv.org/abs/2203.15104 · HTML: https://mlsanity.com/paper/2203.15104/

## 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 (most cited first, 27 of 27)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Local SGD Converges Fast and Communicates Little](https://mlsanity.com/paper/1805.09767/) (2018)
- [Federated Learning Based on Dynamic Regularization](https://mlsanity.com/paper/2111.04263/) (2021)
- [Agnostic Federated Learning](https://mlsanity.com/paper/1902.00146/) (2019)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization](https://mlsanity.com/paper/1909.13014/) (2019)
- [On the Convergence of Stochastic Gradient Descent with Adaptive Stepsizes](https://mlsanity.com/paper/1805.08114/) (2018)
- [FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data](https://mlsanity.com/paper/2005.11418/) (2020)
- [Communication Efficient Distributed Optimization using an Approximate Newton-type Method](https://mlsanity.com/paper/1312.7853/) (2013)
- [Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients](https://mlsanity.com/paper/2102.07053/) (2021)
- [FedSplit: An algorithmic framework for fast federated optimization](https://mlsanity.com/paper/2005.05238/) (2020)
- [Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithms](https://mlsanity.com/paper/1808.07576/) (2018)
- [First Analysis of Local GD on Heterogeneous Data](https://mlsanity.com/paper/1909.04715/) (2019)
- [Federated Composite Optimization](https://mlsanity.com/paper/2011.08474/) (2020)
- [FedDR -- Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization](https://mlsanity.com/paper/2103.03452/) (2021)
- [Douglas-Rachford splitting and ADMM for nonconvex optimization: tight convergence results](https://mlsanity.com/paper/1709.05747/) (2017)
- [Global convergence of splitting methods for nonconvex composite optimization](https://mlsanity.com/paper/1407.0753/) (2014)
- [Douglas-Rachford splitting for nonconvex optimization with application to nonconvex feasibility problems](https://mlsanity.com/paper/1409.8444/) (2014)
- [An automatic system to detect equivalence between iterative algorithms](https://mlsanity.com/paper/2105.04684/) (2021)
- [Self Equivalence of the Alternating Direction Method of Multipliers](https://mlsanity.com/paper/1407.7400/) (2014)
- [A Control-Theoretic Approach to Analysis and Parameter Selection of Douglas-Rachford Splitting](https://mlsanity.com/paper/1903.11525/) (2019)
- [Distributed and Localized Model Predictive Control. Part I: Synthesis and Implementation](https://mlsanity.com/paper/2110.07010/) (2021)

## Built on by (most cited first, 30 of 36)

- [FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy](https://mlsanity.com/paper/2302.10429/) (2023)
- [Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape](https://mlsanity.com/paper/2305.11584/) (2023)
- [Improving the Model Consistency of Decentralized Federated Learning](https://mlsanity.com/paper/2302.04083/) (2023)
- [Federated Temporal Difference Learning with Linear Function Approximation under Environmental Heterogeneity](https://mlsanity.com/paper/2302.02212/) (2023)
- [Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initialization](https://mlsanity.com/paper/2306.05706/) (2023)
- [Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates](https://mlsanity.com/paper/2310.19807/) (2023)
- [FedSysID: A Federated Approach to Sample-Efficient System Identification](https://mlsanity.com/paper/2211.14393/) (2022)
- [Fusion of Global and Local Knowledge for Personalized Federated Learning](https://mlsanity.com/paper/2302.11051/) (2023)
- [Momentum for the Win: Collaborative Federated Reinforcement Learning across Heterogeneous Environments](https://mlsanity.com/paper/2405.19499/) (2024)
- [Composite federated learning with heterogeneous data](https://mlsanity.com/paper/2309.01795/) (2023)
- [Collaborative Bayesian Optimization via Wasserstein Barycenters](https://mlsanity.com/paper/2504.10770/) (2025)
- [DFedADMM: Dual Constraints Controlled Model Inconsistency for Decentralized Federated Learning](https://mlsanity.com/paper/2308.08290/) (2023)
- [Knowledge Adaptation as Posterior Correction](https://mlsanity.com/paper/2506.14262/) (2025)
- [Nonconvex Federated Learning on Compact Smooth Submanifolds With Heterogeneous Data](https://mlsanity.com/paper/2406.08465/) (2024)
- [FedADMM-InSa: An Inexact and Self-Adaptive ADMM for Federated Learning](https://mlsanity.com/paper/2402.13989/) (2024)
- [Asynchronous Heterogeneous Linear Quadratic Regulator Design](https://mlsanity.com/paper/2404.09061/) (2024)
- [Connecting Federated ADMM to Bayes](https://mlsanity.com/paper/2501.17325/) (2025)
- [FedCanon: Non-Convex Composite Federated Learning with Efficient Proximal Operation on Heterogeneous Data](https://mlsanity.com/paper/2504.11903/) (2025)
- [Communication-Efficient Heterogeneous Federated Learning with Generalized Heavy-Ball Momentum](https://mlsanity.com/paper/2311.18578/) (2023)
- [Distributed Event-Based Learning via ADMM](https://mlsanity.com/paper/2405.10618/) (2024)
- [Federated ADMM from Bayesian Duality](https://mlsanity.com/paper/2506.13150/) (2025)
- [FedBC: Calibrating Global and Local Models via Federated Learning Beyond Consensus](https://mlsanity.com/paper/2206.10815/) (2022)
- [A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needs](https://mlsanity.com/paper/2409.18915/) (2024)
- [Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy](https://mlsanity.com/paper/2408.04315/) (2024)
- [Controlling Participation in Federated Learning with Feedback](https://mlsanity.com/paper/2411.19242/) (2024)
- [FedAPM: Federated Learning via ADMM with Partial Model Personalization](https://mlsanity.com/paper/2506.04672/) (2025)
- [First-Order Softmax Weighted Switching Gradient Method for Distributed Stochastic Minimax Optimization with Stochastic Constraints](https://mlsanity.com/paper/2603.05774/) (2026)
- [On the Gradient Heterogeneity Dynamics of Adversarially Robust Federated Regression](https://mlsanity.com/paper/2609.25705/) (2026)
- [Federated Frank-Wolfe Algorithm](https://mlsanity.com/paper/2408.10090/) (2024)
- [Convergent Differential Privacy Analysis for General Federated Learning](https://mlsanity.com/paper/2408.15621/) (2024)

## 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.