# Accelerated Federated Learning with Decoupled Adaptive Optimization

arXiv: https://arxiv.org/abs/2207.07223 · HTML: https://mlsanity.com/paper/2207.07223/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 53 of its 101 references that are in the map.
- First posted on arXiv 14 Jul 2022.

## Builds on (most cited first, 30 of 53)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [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)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Federated Learning Based on Dynamic Regularization](https://mlsanity.com/paper/2111.04263/) (2021)
- [The Non-IID Data Quagmire of Decentralized Machine Learning](https://mlsanity.com/paper/1910.00189/) (2019)
- [Personalized Federated Learning with First Order Model Optimization](https://mlsanity.com/paper/2012.08565/) (2020)
- [FedBABU: Towards Enhanced Representation for Federated Image Classification](https://mlsanity.com/paper/2106.06042/) (2021)
- [FetchSGD: Communication-Efficient Federated Learning with Sketching](https://mlsanity.com/paper/2007.07682/) (2020)
- [Expanding the Reach of Federated Learning by Reducing Client Resource Requirements](https://mlsanity.com/paper/1812.07210/) (2018)
- [Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning](https://mlsanity.com/paper/2008.03606/) (2020)
- [Minibatch vs Local SGD for Heterogeneous Distributed Learning](https://mlsanity.com/paper/2006.04735/) (2020)
- [FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learning](https://mlsanity.com/paper/2108.06098/) (2021)
- [Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing](https://mlsanity.com/paper/2006.09365/) (2020)
- [Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients](https://mlsanity.com/paper/2102.07053/) (2021)
- [SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum](https://mlsanity.com/paper/1910.00643/) (2019)
- [FedSplit: An algorithmic framework for fast federated optimization](https://mlsanity.com/paper/2005.05238/) (2020)
- [Robbing the Fed: Directly Obtaining Private Data in Federated Learning with Modified Models](https://mlsanity.com/paper/2110.13057/) (2021)
- [Federated Learning for Keyword Spotting](https://mlsanity.com/paper/1810.05512/) (2018)
- [Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization](https://mlsanity.com/paper/2002.11364/) (2020)
- [Federated Bayesian Optimization via Thompson Sampling](https://mlsanity.com/paper/2010.10154/) (2020)
- [Accelerating Federated Learning via Momentum Gradient Descent](https://mlsanity.com/paper/1910.03197/) (2019)
- [Fast-Convergent Federated Learning with Adaptive Weighting](https://mlsanity.com/paper/2012.00661/) (2020)
- [From Distributed Machine Learning to Federated Learning: A Survey](https://mlsanity.com/paper/2104.14362/) (2021)
- [What Do We Mean by Generalization in Federated Learning?](https://mlsanity.com/paper/2110.14216/) (2021)
- [Federated Composite Optimization](https://mlsanity.com/paper/2011.08474/) (2020)

## Built on by (most cited first, 23 of 23)

- [Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization](https://mlsanity.com/paper/2310.15080/) (2023)
- [Momentum Benefits Non-IID Federated Learning Simply and Provably](https://mlsanity.com/paper/2306.16504/) (2023)
- [Reducing Communication for Split Learning by Randomized Top-k Sparsification](https://mlsanity.com/paper/2305.18469/) (2023)
- [Multi-Job Intelligent Scheduling with Cross-Device Federated Learning](https://mlsanity.com/paper/2211.13430/) (2022)
- [Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly](https://mlsanity.com/paper/2310.03150/) (2023)
- [AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices](https://mlsanity.com/paper/2312.10935/) (2023)
- [Trustworthy Federated Learning: Privacy, Security, and Beyond](https://mlsanity.com/paper/2411.01583/) (2024)
- [FADAS: Towards Federated Adaptive Asynchronous Optimization](https://mlsanity.com/paper/2407.18365/) (2024)
- [Efficient Asynchronous Federated Learning with Sparsification and Quantization](https://mlsanity.com/paper/2312.15186/) (2023)
- [Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning](https://mlsanity.com/paper/2403.19178/) (2024)
- [Making Batch Normalization Great in Federated Deep Learning](https://mlsanity.com/paper/2303.06530/) (2023)
- [Federated Zeroth-Order Optimization using Trajectory-Informed Surrogate Gradients](https://mlsanity.com/paper/2308.04077/) (2023)
- [Bridging Symmetry and Robustness: On the Role of Equivariance in Enhancing Adversarial Robustness](https://mlsanity.com/paper/2510.16171/) (2025)
- [Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces](https://mlsanity.com/paper/2412.01079/) (2024)
- [FedLion: Faster Adaptive Federated Optimization with Fewer Communication](https://mlsanity.com/paper/2402.09941/) (2024)
- [Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data](https://mlsanity.com/paper/2408.05678/) (2024)
- [Fisher Information-based Efficient Curriculum Federated Learning with Large Language Models](https://mlsanity.com/paper/2410.00131/) (2024)
- [V2X-Boosted Federated Learning for Cooperative Intelligent Transportation Systems with Contextual Client Selection](https://mlsanity.com/paper/2305.11654/) (2023)
- [Federated brain tumor segmentation: an extensive benchmark](https://mlsanity.com/paper/2410.17265/) (2024)
- [PDSL: Privacy-Preserved Decentralized Stochastic Learning with Heterogeneous Data Distribution](https://mlsanity.com/paper/2503.23726/) (2025)
- [Efficient Federated Learning with Heterogeneous Data and Adaptive Dropout](https://mlsanity.com/paper/2507.10430/) (2025)
- [Tackling Computational Heterogeneity in FL: A Few Theoretical Insights](https://mlsanity.com/paper/2307.06283/) (2023)
- [Pushing the (Decision) Boundaries: Dynamically Calibrating Differentially Private Noise to Explainability in Federated Learning](https://mlsanity.com/paper/2609.03851/) (2026)

## Abstract (arXiv)

The federated learning (FL) framework enables edge clients to collaboratively learn a shared inference model while keeping privacy of training data on clients. Recently, many heuristics efforts have been made to generalize centralized adaptive optimization methods, such as SGDM, Adam, AdaGrad, etc., to federated settings for improving convergence and accuracy. However, there is still a paucity of theoretical principles on where to and how to design and utilize adaptive optimization methods in federated settings. This work aims to develop novel adaptive optimization methods for FL from the perspective of dynamics of ordinary differential equations (ODEs). First, an analytic framework is established to build a connection between federated optimization methods and decompositions of ODEs of corresponding centralized optimizers. Second, based on this analytic framework, a momentum decoupling adaptive optimization method, FedDA, is developed to fully utilize the global momentum on each local iteration and accelerate the training convergence. Last but not least, full batch gradients are utilized to mimic centralized optimization in the end of the training process to ensure the convergence and overcome the possible inconsistency caused by adaptive optimization methods.