# Adaptive Consensus ADMM for Distributed Optimization

arXiv: https://arxiv.org/abs/1706.02869 · HTML: https://mlsanity.com/paper/1706.02869/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2017.

## Built on by (most cited first, 5 of 5)

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- [Secure Distributed/Federated Learning: Prediction-Privacy Trade-Off for Multi-Agent System](https://mlsanity.com/paper/2205.04855/) (2022)
- [Compressed Sensor Caching and Collaborative Sparse Data Recovery with Anchor Alignment](https://mlsanity.com/paper/2406.10137/) (2024)

## Abstract (arXiv)

The alternating direction method of multipliers (ADMM) is commonly used for distributed model fitting problems, but its performance and reliability depend strongly on user-defined penalty parameters. We study distributed ADMM methods that boost performance by using different fine-tuned algorithm parameters on each worker node. We present a O(1/k) convergence rate for adaptive ADMM methods with node-specific parameters, and propose adaptive consensus ADMM (ACADMM), which automatically tunes parameters without user oversight.