# Coded Stochastic ADMM for Decentralized Consensus Optimization with Edge Computing

arXiv: https://arxiv.org/abs/2010.00914 · HTML: https://mlsanity.com/paper/2010.00914/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Oct 2020.

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

- [Federated Learning over Wireless IoT Networks with Optimized Communication and Resources](https://mlsanity.com/paper/2110.11775/) (2021)
- [Provable Privacy Advantages of Decentralized Federated Learning via Distributed Optimization](https://mlsanity.com/paper/2407.09324/) (2024)
- [Linear Regression with Distributed Learning: A Generalization Error Perspective](https://mlsanity.com/paper/2101.09001/) (2021)
- [Asynchronous Parallel Incremental Block-Coordinate Descent for Decentralized Machine Learning](https://mlsanity.com/paper/2202.03263/) (2022)
- [Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in Edge Industrial IoT](https://mlsanity.com/paper/2107.00481/) (2021)

## Abstract (arXiv)

Big data, including applications with high security requirements, are often collected and stored on multiple heterogeneous devices, such as mobile devices, drones and vehicles. Due to the limitations of communication costs and security requirements, it is of paramount importance to extract information in a decentralized manner instead of aggregating data to a fusion center. To train large-scale machine learning models, edge/fog computing is often leveraged as an alternative to centralized learning. We consider the problem of learning model parameters in a multi-agent system with data locally processed via distributed edge nodes. A class of mini-batch stochastic alternating direction method of multipliers (ADMM) algorithms is explored to develop the distributed learning model. To address two main critical challenges in distributed networks, i.e., communication bottleneck and straggler nodes (nodes with slow responses), error-control-coding based stochastic incremental ADMM is investigated. Given an appropriate mini-batch size, we show that the mini-batch stochastic ADMM based method converges in a rate of O(frac1√k), where k denotes the number of iterations. Through numerical experiments, it is revealed that the proposed algorithm is communication-efficient, rapidly responding and robust in the presence of straggler nodes compared with state of the art algorithms.