# QC-ODKLA: Quantized and Communication-Censored Online Decentralized Kernel Learning via Linearized ADMM

arXiv: https://arxiv.org/abs/2208.02777 · HTML: https://mlsanity.com/paper/2208.02777/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 61 references that are in the map.
- First posted on arXiv 4 Aug 2022.

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

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Sparsified SGD with Memory](https://mlsanity.com/paper/1809.07599/) (2018)
- [LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning](https://mlsanity.com/paper/1805.09965/) (2018)
- [Revisiting the Nystrom Method for Improved Large-Scale Machine Learning](https://mlsanity.com/paper/1303.1849/) (2013)
- [Scalable Kernel Methods via Doubly Stochastic Gradients](https://mlsanity.com/paper/1407.5599/) (2014)
- [Distributed Online Optimization in Dynamic Environments Using Mirror Descent](https://mlsanity.com/paper/1609.02845/) (2016)
- [Accelerated Distributed Nesterov Gradient Descent](https://mlsanity.com/paper/1705.07176/) (2017)
- [Double Quantization for Communication-Efficient Distributed Optimization](https://mlsanity.com/paper/1805.10111/) (2018)
- [Parsimonious Online Learning with Kernels via Sparse Projections in Function Space](https://mlsanity.com/paper/1612.04111/) (2016)
- [Online Distributed Learning Over Networks in RKH Spaces Using Random Fourier Features](https://mlsanity.com/paper/1703.08131/) (2017)
- [Decentralised Learning with Random Features and Distributed Gradient Descent](https://mlsanity.com/paper/2007.00360/) (2020)
- [Distributed Online Learning with Multiple Kernels](https://mlsanity.com/paper/2102.12733/) (2021)
- [COKE: Communication-Censored Decentralized Kernel Learning](https://mlsanity.com/paper/2001.10133/) (2020)
- [On Distributed Online Convex Optimization with Sublinear Dynamic Regret and Fit](https://mlsanity.com/paper/2001.03166/) (2020)
- [Quantized Consensus ADMM for Multi-Agent Distributed Optimization](https://mlsanity.com/paper/1510.08736/) (2015)
- [Online Learning over Dynamic Graphs via Distributed Proximal Gradient Algorithm](https://mlsanity.com/paper/1905.07018/) (2019)
- [Analysis of a Reduced-Communication Diffusion LMS Algorithm](https://mlsanity.com/paper/1408.5845/) (2014)
- [Decentralized Online Learning: Take Benefits from Others' Data without Sharing Your Own to Track Global Trend](https://mlsanity.com/paper/1901.10593/) (2019)
- [Regret Bounds for Decentralized Learning in Cooperative Multi-Agent Dynamical Systems](https://mlsanity.com/paper/2001.10122/) (2020)
- [On reducing the communication cost of the diffusion LMS algorithm](https://mlsanity.com/paper/1711.11423/) (2017)
- [Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free](https://mlsanity.com/paper/1902.06332/) (2019)

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

- [LEAF: A Learning-Enabled ADMM Framework for Accelerated Convex Optimization](https://mlsanity.com/paper/2606.08993/) (2026)
- [Decentralized Online Learning for Random Inverse Problems Over Graphs](https://mlsanity.com/paper/2303.11789/) (2023)
- [Multi-Worker Selection based Distributed Swarm Learning for Edge IoT with Non-i.i.d. Data](https://mlsanity.com/paper/2509.18367/) (2025)

## Abstract (arXiv)

This paper focuses on online kernel learning over a decentralized network. Each agent in the network receives continuous streaming data locally and works collaboratively to learn a nonlinear prediction function that is globally optimal in the reproducing kernel Hilbert space with respect to the total instantaneous costs of all agents. In order to circumvent the curse of dimensionality issue in traditional online kernel learning, we utilize random feature (RF) mapping to convert the non-parametric kernel learning problem into a fixed-length parametric one in the RF space. We then propose a novel learning framework named Online Decentralized Kernel learning via Linearized ADMM (ODKLA) to efficiently solve the online decentralized kernel learning problem. To further improve the communication efficiency, we add the quantization and censoring strategies in the communication stage and develop the Quantized and Communication-censored ODKLA (QC-ODKLA) algorithm. We theoretically prove that both ODKLA and QC-ODKLA can achieve the optimal sublinear regret O(√T) over T time slots. Through numerical experiments, we evaluate the learning effectiveness, communication, and computation efficiencies of the proposed methods.