# Resource-Aware Asynchronous Online Federated Learning for Nonlinear Regression

arXiv: https://arxiv.org/abs/2111.13931 · HTML: https://mlsanity.com/paper/2111.13931/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 26 references that are in the map.
- First posted on arXiv 27 Nov 2021.

## Builds on (most cited first, 16 of 16)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (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)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://mlsanity.com/paper/1903.02891/) (2019)
- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [Energy Efficient Federated Learning Over Wireless Communication Networks](https://mlsanity.com/paper/1911.02417/) (2019)
- [Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregation](https://mlsanity.com/paper/1903.07424/) (2019)
- [TiFL: A Tier-based Federated Learning System](https://mlsanity.com/paper/2001.09249/) (2020)
- [Age of Information: An Introduction and Survey](https://mlsanity.com/paper/2007.08564/) (2020)
- [FedADC: Accelerated Federated Learning with Drift Control](https://mlsanity.com/paper/2012.09102/) (2020)
- [Communication-Efficient Online Federated Learning Framework for Nonlinear Regression](https://mlsanity.com/paper/2110.06556/) (2021)
- [Asynchronous Federated Learning for Sensor Data with Concept Drift](https://mlsanity.com/paper/2109.00151/) (2021)
- [Efficient KLMS and KRLS Algorithms: A Random Fourier Feature Perspective](https://mlsanity.com/paper/1606.03685/) (2016)

## Built on by (most cited first, 5 of 5)

- [Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning](https://mlsanity.com/paper/2410.21547/) (2024)
- [Federated Online and Bandit Convex Optimization](https://mlsanity.com/paper/2311.17586/) (2023)
- [Asynchronous Multi-Model Dynamic Federated Learning over Wireless Networks: Theory, Modeling, and Optimization](https://mlsanity.com/paper/2305.13503/) (2023)
- [Review of Mathematical Optimization in Federated Learning](https://mlsanity.com/paper/2412.01630/) (2024)
- [Asynchronous Online Federated Learning with Reduced Communication Requirements](https://mlsanity.com/paper/2303.15226/) (2023)

## Abstract (arXiv)

Many assumptions in the federated learning literature present a best-case scenario that can not be satisfied in most real-world applications. An asynchronous setting reflects the realistic environment in which federated learning methods must be able to operate reliably. Besides varying amounts of non-IID data at participants, the asynchronous setting models heterogeneous client participation due to available computational power and battery constraints and also accounts for delayed communications between clients and the server. To reduce the communication overhead associated with asynchronous online federated learning (ASO-Fed), we use the principles of partial-sharing-based communication. In this manner, we reduce the communication load of the participants and, therefore, render participation in the learning task more accessible. We prove the convergence of the proposed ASO-Fed and provide simulations to analyze its behavior further. The simulations reveal that, in the asynchronous setting, it is possible to achieve the same convergence as the federated stochastic gradient (Online-FedSGD) while reducing the communication tenfold.