# Asynchronous and Stochastic Distributed Resource Allocation

arXiv: https://arxiv.org/abs/2509.01172 · HTML: https://mlsanity.com/paper/2509.01172/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 1 Sep 2025.

## Builds on (most cited first, 10 of 10)

- [Advances in Asynchronous Parallel and Distributed Optimization](https://mlsanity.com/paper/2006.13838/) (2020)
- [Optimal Time Complexities of Parallel Stochastic Optimization Methods Under a Fixed Computation Model](https://mlsanity.com/paper/2305.12387/) (2023)
- [Multi-agent Performative Prediction with Greedy Deployment and Consensus Seeking Agents](https://mlsanity.com/paper/2209.03811/) (2022)
- [AsGrad: A Sharp Unified Analysis of Asynchronous-SGD Algorithms](https://mlsanity.com/paper/2310.20452/) (2023)
- [Asynchronous Multi-Agent Primal-Dual Optimization](https://mlsanity.com/paper/1606.01993/) (2016)
- [Resilient Primal-Dual Optimization Algorithms for Distributed Resource Allocation](https://mlsanity.com/paper/2001.00612/) (2020)
- [Asynchronous Distributed Voltage Control in Active Distribution Networks](https://mlsanity.com/paper/1908.06548/) (2019)
- [Socially Optimal Energy Usage via Adaptive Pricing](https://mlsanity.com/paper/2310.13254/) (2023)
- [Distributed Optimization with Coupling Constraints](https://mlsanity.com/paper/2102.12989/) (2021)
- [Totally Asynchronous Primal-Dual Convex Optimization in Blocks](https://mlsanity.com/paper/2107.10338/) (2021)

## Abstract (arXiv)

This work proposes and studies the distributed resource allocation problem in asynchronous and stochastic settings. We consider a distributed system with multiple workers and a coordinating server with heterogeneous computation and communication times. We explore an approximate stochastic primal-dual approach with the aim of 1) adhering to the resource budget constraints, 2) allowing for the asynchronicity between the workers and the server, and 3) relying on the locally available stochastic gradients. We analyze our Asynchronous stochastic Primal-Dual (Asyn-PD) algorithm and prove its convergence in the second moment to the saddle point solution of the approximate problem at the rate of O(1/t), where t is the iteration number. Furthermore, we verify our algorithm numerically to validate the analytically derived convergence results, and demonstrate the advantages of utilizing our asynchronous algorithm rather than deploying a synchronous algorithm where the server must wait until it gets update from all workers.