# Recycled ADMM: Improve Privacy and Accuracy with Less Computation in Distributed Algorithms

arXiv: https://arxiv.org/abs/1810.03197 · HTML: https://mlsanity.com/paper/1810.03197/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2018.

## Built on by (most cited first, 8 of 8)

- [Federated Learning via Inexact ADMM](https://mlsanity.com/paper/2204.10607/) (2022)
- [FedGiA: An Efficient Hybrid Algorithm for Federated Learning](https://mlsanity.com/paper/2205.01438/) (2022)
- [Communication efficient privacy-preserving distributed optimization using adaptive differential quantization](https://mlsanity.com/paper/2105.14416/) (2021)
- [Federated Stochastic Primal-dual Learning with Differential Privacy](https://mlsanity.com/paper/2204.12284/) (2022)
- [From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges](https://mlsanity.com/paper/2503.07505/) (2025)
- [Exact Penalty Method for Federated Learning](https://mlsanity.com/paper/2208.11231/) (2022)
- [Federated Learning with Reduced Information Leakage and Computation](https://mlsanity.com/paper/2310.06341/) (2023)
- [Privacy-Aware Randomized Quantization via Linear Programming](https://mlsanity.com/paper/2406.02599/) (2024)

## Abstract (arXiv)

Alternating direction method of multiplier (ADMM) is a powerful method to solve decentralized convex optimization problems. In distributed settings, each node performs computation with its local data and the local results are exchanged among neighboring nodes in an iterative fashion. During this iterative process the leakage of data privacy arises and can accumulate significantly over many iterations, making it difficult to balance the privacy-utility tradeoff. In this study we propose Recycled ADMM (R-ADMM), where a linear approximation is applied to every even iteration, its solution directly calculated using only results from the previous, odd iteration. It turns out that under such a scheme, half of the updates incur no privacy loss and require much less computation compared to the conventional ADMM. We obtain a sufficient condition for the convergence of R-ADMM and provide the privacy analysis based on objective perturbation.