# Communication-Efficient Distributed Online Learning with Kernels

arXiv: https://arxiv.org/abs/1911.12899 · HTML: https://mlsanity.com/paper/1911.12899/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2019.

## Built on by (most cited first, 4 of 4)

- [Lessons from Generalization Error Analysis of Federated Learning: You May Communicate Less Often!](https://mlsanity.com/paper/2306.05862/) (2023)
- [Little is Enough: Boosting Privacy by Sharing Only Hard Labels in Federated Semi-Supervised Learning](https://mlsanity.com/paper/2310.05696/) (2023)
- [Whom to Trust? Adaptive Collaboration in Personalized Federated Learning](https://mlsanity.com/paper/2507.00259/) (2025)
- [Beyond Parameter Aggregation: Semantic Consensus for Federated Fine-Tuning of LLMs](https://mlsanity.com/paper/2605.11857/) (2026)

## Abstract (arXiv)

We propose an efficient distributed online learning protocol for low-latency real-time services. It extends a previously presented protocol to kernelized online learners that represent their models by a support vector expansion. While such learners often achieve higher predictive performance than their linear counterparts, communicating the support vector expansions becomes inefficient for large numbers of support vectors. The proposed extension allows for a larger class of online learning algorithms---including those alleviating the problem above through model compression. In addition, we characterize the quality of the proposed protocol by introducing a novel criterion that requires the communication to be bounded by the loss suffered.