# Asynchronous Diffusion Learning with Agent Subsampling and Local Updates

arXiv: https://arxiv.org/abs/2402.05529 · HTML: https://mlsanity.com/paper/2402.05529/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 30 references that are in the map.
- First posted on arXiv 8 Feb 2024.

## Builds on (most cited first, 6 of 6)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Distributed Learning in Non-Convex Environments -- Part I: Agreement at a Linear Rate](https://mlsanity.com/paper/1907.01848/) (2019)
- [On the Learning Behavior of Adaptive Networks - Part I: Transient Analysis](https://mlsanity.com/paper/1312.7581/) (2013)
- [Asynchronous Gradient-Push](https://mlsanity.com/paper/1803.08950/) (2018)
- [Asynchronous Adaptation and Learning over Networks --- Part I: Modeling and Stability Analysis](https://mlsanity.com/paper/1312.5434/) (2013)
- [Analysis of a Reduced-Communication Diffusion LMS Algorithm](https://mlsanity.com/paper/1408.5845/) (2014)

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

- [Diffusion Learning with Partial Agent Participation and Local Updates](https://mlsanity.com/paper/2505.11307/) (2025)

## Abstract (arXiv)

In this work, we examine a network of agents operating asynchronously, aiming to discover an ideal global model that suits individual local datasets. Our assumption is that each agent independently chooses when to participate throughout the algorithm and the specific subset of its neighbourhood with which it will cooperate at any given moment. When an agent chooses to take part, it undergoes multiple local updates before conveying its outcomes to the sub-sampled neighbourhood. Under this setup, we prove that the resulting asynchronous diffusion strategy is stable in the mean-square error sense and provide performance guarantees specifically for the federated learning setting. We illustrate the findings with numerical simulations.