# Quantized Distributed Online Projection-free Convex Optimization

arXiv: https://arxiv.org/abs/2302.13559 · HTML: https://mlsanity.com/paper/2302.13559/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2023.

## Built on by (most cited first, 2 of 2)

- [Distributed Online Convex Optimization with Efficient Communication: Improved Algorithm and Lower bounds](https://mlsanity.com/paper/2601.04907/) (2026)
- [Distributed Online Bandit Nonconvex Optimization with One-Point Residual Feedback via Dynamic Regret](https://mlsanity.com/paper/2409.15680/) (2024)

## Abstract (arXiv)

This paper considers online distributed convex constrained optimization over a time-varying multi-agent network. Agents in this network cooperate to minimize the global objective function through information exchange with their neighbors and local computation. Since the capacity or bandwidth of communication channels often is limited, a random quantizer is introduced to reduce the transmission bits. Through incorporating this quantizer, we develop a quantized distributed online projection-free optimization algorithm, which can achieve the saving of communication resources and computational costs. For different parameter settings of the quantizer, we establish the corresponding dynamic regret upper bounds of the proposed algorithm and reveal the trade-off between the convergence performance and the quantization effect. Finally, the theoretical results are illustrated by the simulation of distributed online linear regression problem.