# Communication-Efficient Online Federated Learning Framework for Nonlinear Regression

arXiv: https://arxiv.org/abs/2110.06556 · HTML: https://mlsanity.com/paper/2110.06556/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 13 Oct 2021.

## Builds on (most cited first, 12 of 12)

- [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 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)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://mlsanity.com/paper/1903.02891/) (2019)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge](https://mlsanity.com/paper/1804.08333/) (2018)
- [Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges](https://mlsanity.com/paper/1908.06847/) (2019)
- [Fast-Convergent Federated Learning](https://mlsanity.com/paper/2007.13137/) (2020)
- [Communication-Efficient Federated Learning with Compensated Overlap-FedAvg](https://mlsanity.com/paper/2012.06706/) (2020)
- [Optimal Importance Sampling for Federated Learning](https://mlsanity.com/paper/2010.13600/) (2020)

## Built on by (most cited first, 16 of 16)

- [Tighter Regret Analysis and Optimization of Online Federated Learning](https://mlsanity.com/paper/2205.06491/) (2022)
- [Personalized Online Federated Learning with Multiple Kernels](https://mlsanity.com/paper/2311.05108/) (2023)
- [Towards Fairer and More Efficient Federated Learning via Multidimensional Personalized Edge Models](https://mlsanity.com/paper/2302.04464/) (2023)
- [Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often!](https://mlsanity.com/paper/2306.05862/) (2023)
- [Federated Online and Bandit Convex Optimization](https://mlsanity.com/paper/2311.17586/) (2023)
- [Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learning](https://mlsanity.com/paper/2202.03070/) (2022)
- [Resource-Aware Asynchronous Online Federated Learning for Nonlinear Regression](https://mlsanity.com/paper/2111.13931/) (2021)
- [FedJudge: Federated Legal Large Language Model](https://mlsanity.com/paper/2309.08173/) (2023)
- [Resilience in Online Federated Learning: Mitigating Model-Poisoning Attacks via Partial Sharing](https://mlsanity.com/paper/2403.13108/) (2024)
- [Asynchronous Online Federated Learning with Reduced Communication Requirements](https://mlsanity.com/paper/2303.15226/) (2023)
- [Budgeted Online Model Selection and Fine-Tuning via Federated Learning](https://mlsanity.com/paper/2401.10478/) (2024)
- [Noise-Robust and Resource-Efficient ADMM-based Federated Learning](https://mlsanity.com/paper/2409.13451/) (2024)
- [Partial Model Sharing Improves Byzantine Resilience in Federated Conformal Prediction](https://mlsanity.com/paper/2605.11684/) (2026)
- [Moreau Envelope ADMM for Decentralized Weakly Convex Optimization](https://mlsanity.com/paper/2308.16752/) (2023)
- [Federated Reinforcement Learning in Heterogeneous Environments](https://mlsanity.com/paper/2507.14487/) (2025)
- [Communication-Efficient Byzantine-Robust Federated Conformal Prediction via Partial Sharing](https://mlsanity.com/paper/2602.18396/) (2026)

## Abstract (arXiv)

Federated learning (FL) literature typically assumes that each client has a fixed amount of data, which is unrealistic in many practical applications. Some recent works introduced a framework for online FL (Online-Fed) wherein clients perform model learning on streaming data and communicate the model to the server; however, they do not address the associated communication overhead. As a solution, this paper presents a partial-sharing-based online federated learning framework (PSO-Fed) that enables clients to update their local models using continuous streaming data and share only portions of those updated models with the server. During a global iteration of PSO-Fed, non-participant clients have the privilege to update their local models with new data. Here, we consider a global task of kernel regression, where clients use a random Fourier features-based kernel LMS on their data for local learning. We examine the mean convergence of the PSO-Fed for kernel regression. Experimental results show that PSO-Fed can achieve competitive performance with a significantly lower communication overhead than Online-Fed.