# Asynchronous Gossip-Based Random Projection Algorithms Over Networks

arXiv: https://arxiv.org/abs/1304.1757 · HTML: https://mlsanity.com/paper/1304.1757/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2013.

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

- [From Federated Learning to X-Learning: Breaking the Barriers of Decentrality Through Random Walks](https://mlsanity.com/paper/2509.03709/) (2025)

## Abstract (arXiv)

We consider a fully distributed constrained convex optimization problem over a multi-agent (no central coordinator) network. We propose an asynchronous gossip-based random projection (GRP) algorithm that solves the distributed problem using only local communications and computations. We analyze the convergence properties of the algorithm for an uncoordinated diminishing stepsize and a constant stepsize. For a diminishing stepsize, we prove that the iterates of all agents converge to the same optimal point with probability 1. For a constant stepsize, we establish an error bound on the expected distance from the iterates of the algorithm to the optimal point. We also provide simulation results on a distributed robust model predictive control problem.