# Decentralized ADMM with Compressed and Event-Triggered Communication

arXiv: https://arxiv.org/abs/2301.09981 · HTML: https://mlsanity.com/paper/2301.09981/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2023.

## Built on by (most cited first, 1 of 1)

- [Distributed Event-Based Learning via ADMM](https://mlsanity.com/paper/2405.10618/) (2024)

## Abstract (arXiv)

This paper focuses on the decentralized optimization problem, where agents in a network cooperate to minimize the sum of their local objective functions by information exchange and local computation. Based on the alternating direction method of multipliers (ADMM), we propose CC-DQM, a communication-efficient decentralized second-order optimization algorithm that combines compressed communication with event-triggered communication. Specifically, agents are allowed to transmit the compressed message only when the current primal variables have changed greatly compared to its last estimate. Moreover, to relieve the computation cost, the update of Hessian is scheduled by the trigger condition. To maintain exact linear convergence under compression, we compress the difference between the information to be transmitted and its estimate by a general contractive compressor. Theoretical analysis shows that CC-DQM can still achieve an exact linear convergence, despite the existence of compression error and intermittent communication, if the objective functions are strongly convex and smooth. Finally, we validate the performance of CC-DQM by numerical experiments.