# Asynchronous Multi-Server Federated Learning for Geo-Distributed Clients

arXiv: https://arxiv.org/abs/2406.01439 · HTML: https://mlsanity.com/paper/2406.01439/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 38 references that are in the map.
- First posted on arXiv 3 Jun 2024.

## Builds on (most cited first, 21 of 21)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [The Future of Digital Health with Federated Learning](https://mlsanity.com/paper/2003.08119/) (2020)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [Client-Edge-Cloud Hierarchical Federated Learning](https://mlsanity.com/paper/1905.06641/) (2019)
- [Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Results](https://mlsanity.com/paper/2001.05647/) (2020)
- [Hierarchical Federated Learning Across Heterogeneous Cellular Networks](https://mlsanity.com/paper/1909.02362/) (2019)
- [Heterogeneity for the Win: One-Shot Federated Clustering](https://mlsanity.com/paper/2103.00697/) (2021)
- [Communication-Efficient Distributed Dual Coordinate Ascent](https://mlsanity.com/paper/1409.1458/) (2014)
- [AsyncFedED: Asynchronous Federated Learning with Euclidean Distance based Adaptive Weight Aggregation](https://mlsanity.com/paper/2205.13797/) (2022)
- [To Cluster, or Not to Cluster: An Analysis of Clusterability Methods](https://mlsanity.com/paper/1808.08317/) (2018)
- [AGIC: Approximate Gradient Inversion Attack on Federated Learning](https://mlsanity.com/paper/2204.13784/) (2022)
- [On the Convergence of Multi-Server Federated Learning with Overlapping Area](https://mlsanity.com/paper/2208.07893/) (2022)
- [Aergia: Leveraging Heterogeneity in Federated Learning Systems](https://mlsanity.com/paper/2210.06154/) (2022)
- [MUDGUARD: Taming Malicious Majorities in Federated Learning using Privacy-Preserving Byzantine-Robust Clustering](https://mlsanity.com/paper/2208.10161/) (2022)
- [Towards Geo-Distributed Machine Learning](https://mlsanity.com/paper/1603.09035/) (2016)
- [Share Secrets for Privacy: Confidential Forecasting with Vertical Federated Learning](https://mlsanity.com/paper/2405.20761/) (2024)
- [An efficient distributed learning algorithm based on effective local functional approximations](https://mlsanity.com/paper/1310.8418/) (2013)
- [Sky Computing: Accelerating Geo-distributed Computing in Federated Learning](https://mlsanity.com/paper/2202.11836/) (2022)

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

- [Dynamic Topology Optimization for Non-IID Data in Decentralized Learning](https://mlsanity.com/paper/2602.03383/) (2026)
- [Optimization Trade-offs in Asynchronous Federated Learning: A Stochastic Networks Approach](https://mlsanity.com/paper/2603.26231/) (2026)
- [SCOPE-FL: A Strategy-proof Chain-based Optimal pareto efficient Federated Learning System](https://mlsanity.com/paper/2606.18384/) (2026)

## Abstract (arXiv)

Federated learning (FL) systems enable multiple clients to train a machine learning model iteratively through synchronously exchanging the intermediate model weights with a single server. The scalability of such FL systems can be limited by two factors: server idle time due to synchronous communication and the risk of a single server becoming the bottleneck. In this paper, we propose a new FL architecture, to our knowledge, the first multi-server FL system that is entirely asynchronous, and therefore addresses these two limitations simultaneously. Our solution keeps both servers and clients continuously active. As in previous multi-server methods, clients interact solely with their nearest server, ensuring efficient update integration into the model. Differently, however, servers also periodically update each other asynchronously, and never postpone interactions with clients. We compare our solution to three representative baselines - FedAvg, FedAsync and HierFAVG - on the MNIST and CIFAR-10 image classification datasets and on the WikiText-2 language modeling dataset. Our solution converges to similar or higher accuracy levels than previous baselines and requires 61% less time to do so in geo-distributed settings.