# Asynchronous Federated Learning: A Scalable Approach for Decentralized Machine Learning

arXiv: https://arxiv.org/abs/2412.17723 · HTML: https://mlsanity.com/paper/2412.17723/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 60 references that are in the map.
- First posted on arXiv 23 Dec 2024.

## Builds on (most cited first, 30 of 33)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [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)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [A Unified Theory of Decentralized SGD with Changing Topology and Local Updates](https://mlsanity.com/paper/2003.10422/) (2020)
- [Tighter Theory for Local SGD on Identical and Heterogeneous Data](https://mlsanity.com/paper/1909.04746/) (2019)
- [Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2104.07914/) (2021)
- [Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning](https://mlsanity.com/paper/2002.05651/) (2020)
- [Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning](https://mlsanity.com/paper/2101.11203/) (2021)
- [Federated Learning for Smart Healthcare: A Survey](https://mlsanity.com/paper/2111.08834/) (2021)
- [Minibatch vs Local SGD for Heterogeneous Distributed Learning](https://mlsanity.com/paper/2006.04735/) (2020)
- [Random Reshuffling: Simple Analysis with Vast Improvements](https://mlsanity.com/paper/2006.05988/) (2020)
- [Why Random Reshuffling Beats Stochastic Gradient Descent](https://mlsanity.com/paper/1510.08560/) (2015)
- [Random Shuffling Beats SGD after Finite Epochs](https://mlsanity.com/paper/1806.10077/) (2018)
- [How Good is SGD with Random Shuffling?](https://mlsanity.com/paper/1908.00045/) (2019)
- [SGD without Replacement: Sharper Rates for General Smooth Convex Functions](https://mlsanity.com/paper/1903.01463/) (2019)
- [SGD with shuffling: optimal rates without component convexity and large epoch requirements](https://mlsanity.com/paper/2006.06946/) (2020)
- [Closing the convergence gap of SGD without replacement](https://mlsanity.com/paper/2002.10400/) (2020)
- [A Unified Analysis of Federated Learning with Arbitrary Client Participation](https://mlsanity.com/paper/2205.13648/) (2022)
- [Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond](https://mlsanity.com/paper/2110.10342/) (2021)
- [Proximal and Federated Random Reshuffling](https://mlsanity.com/paper/2102.06704/) (2021)
- [On the Convergence of Federated Averaging with Cyclic Client Participation](https://mlsanity.com/paper/2302.03109/) (2023)
- [Tighter Lower Bounds for Shuffling SGD: Random Permutations and Beyond](https://mlsanity.com/paper/2303.07160/) (2023)
- [Random Shuffling Beats SGD Only After Many Epochs on Ill-Conditioned Problems](https://mlsanity.com/paper/2106.06880/) (2021)
- [Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions](https://mlsanity.com/paper/2411.12377/) (2024)
- [A Robust SINDy Approach by Combining Neural Networks and an Integral Form](https://mlsanity.com/paper/2309.07193/) (2023)

## Built on by (most cited first, 3 of 3)

- [Asynchronous Federated Learning with non-convex client objective functions and heterogeneous dataset](https://mlsanity.com/paper/2508.01675/) (2025)
- [HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learning](https://mlsanity.com/paper/2501.09934/) (2025)
- [AlignFed: Alignment-Aware Asynchronous Federated Fine-Tuning for Large Language Models in Heterogeneous Edge Environments](https://mlsanity.com/paper/2606.08197/) (2026)

## Abstract (arXiv)

Federated Learning (FL) has emerged as a powerful paradigm for decentralized machine learning, enabling collaborative model training across diverse clients without sharing raw data. However, traditional FL approaches often face limitations in scalability and efficiency due to their reliance on synchronous client updates, which can result in significant delays and increased communication overhead, particularly in heterogeneous and dynamic environments. To address these challenges in this paper, we propose an Asynchronous Federated Learning (AFL) algorithm, which allows clients to update the global model independently and asynchronously. Our key contributions include a comprehensive convergence analysis of AFL in the presence of client delays and model staleness. By leveraging martingale difference sequence theory and variance bounds, we ensure robust convergence despite asynchronous updates. Assuming strongly convex local objective functions, we establish bounds on gradient variance under random client sampling and derive a recursion formula quantifying the impact of client delays on convergence. Furthermore, we demonstrate the practical applicability of the AFL algorithm by training decentralized linear regression and Support Vector Machine (SVM) based classifiers and compare its results with synchronous FL algorithm to effectively handling non-IID data distributed among clients. The proposed AFL algorithm addresses key limitations of traditional FL methods, such as inefficiency due to global synchronization and susceptibility to client drift. It enhances scalability, robustness, and efficiency in real-world settings with heterogeneous client populations and dynamic network conditions. Our results underscore the potential of AFL to drive advancements indistributed learning systems, particularly for large-scale, privacy-preserving applications in resource-constrained environments.