# Diffusion Learning with Partial Agent Participation and Local Updates

arXiv: https://arxiv.org/abs/2505.11307 · HTML: https://mlsanity.com/paper/2505.11307/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 37 references that are in the map.
- First posted on arXiv 16 May 2025.

## Builds on (most cited first, 24 of 24)

- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimization](https://mlsanity.com/paper/1404.6264/) (2014)
- [Achieving Geometric Convergence for Distributed Optimization over Time-Varying Graphs](https://mlsanity.com/paper/1607.03218/) (2016)
- [D2: Decentralized Training over Decentralized Data](https://mlsanity.com/paper/1803.07068/) (2018)
- [Optimal Client Sampling for Federated Learning](https://mlsanity.com/paper/2010.13723/) (2020)
- [Decentralized Federated Averaging](https://mlsanity.com/paper/2104.11375/) (2021)
- [NEXT: In-Network Nonconvex Optimization](https://mlsanity.com/paper/1602.00591/) (2016)
- [Distributed optimization over time-varying directed graphs](https://mlsanity.com/paper/1303.2289/) (2013)
- [Exact Diffusion for Distributed Optimization and Learning --- Part I: Algorithm Development](https://mlsanity.com/paper/1702.05122/) (2017)
- [Push-Pull Gradient Methods for Distributed Optimization in Networks](https://mlsanity.com/paper/1810.06653/) (2018)
- [On the Influence of Bias-Correction on Distributed Stochastic Optimization](https://mlsanity.com/paper/1903.10956/) (2019)
- [Decentralized Gradient Tracking with Local Steps](https://mlsanity.com/paper/2301.01313/) (2023)
- [Federated Learning under Importance Sampling](https://mlsanity.com/paper/2012.07383/) (2020)
- [A linear algorithm for optimization over directed graphs with geometric convergence](https://mlsanity.com/paper/1803.02503/) (2018)
- [Local Exact-Diffusion for Decentralized Optimization and Learning](https://mlsanity.com/paper/2302.00620/) (2023)
- [Mobile Edge Intelligence and Computing for the Internet of Vehicles](https://mlsanity.com/paper/1906.00400/) (2019)
- [On the Performance of Gradient Tracking with Local Updates](https://mlsanity.com/paper/2210.04757/) (2022)
- [On the Learning Behavior of Adaptive Networks - Part I: Transient Analysis](https://mlsanity.com/paper/1312.7581/) (2013)
- [Distributed Algorithms for Composite Optimization: Unified Framework and Convergence Analysis](https://mlsanity.com/paper/2002.11534/) (2020)
- [Edge-Computing-Enabled Smart Cities: A Comprehensive Survey](https://mlsanity.com/paper/1909.08747/) (2019)
- [Edge Intelligence for Empowering IoT-based Healthcare Systems](https://mlsanity.com/paper/2103.12144/) (2021)
- [Asynchronous Adaptation and Learning over Networks --- Part I: Modeling and Stability Analysis](https://mlsanity.com/paper/1312.5434/) (2013)
- [A Linearly Convergent Proximal Gradient Algorithm for Decentralized Optimization](https://mlsanity.com/paper/1905.07996/) (2019)
- [Asynchronous Diffusion Learning with Agent Subsampling and Local Updates](https://mlsanity.com/paper/2402.05529/) (2024)

## Abstract (arXiv)

Diffusion learning is a framework that endows edge devices with advanced intelligence. By processing and analyzing data locally and allowing each agent to communicate with its immediate neighbors, diffusion effectively protects the privacy of edge devices, enables real-time response, and reduces reliance on central servers. However, traditional diffusion learning relies on communication at every iteration, leading to communication overhead, especially with large learning models. Furthermore, the inherent volatility of edge devices, stemming from power outages or signal loss, poses challenges to reliable communication between neighboring agents. To mitigate these issues, this paper investigates an enhanced diffusion learning approach incorporating local updates and partial agent participation. Local updates will curtail communication frequency, while partial agent participation will allow for the inclusion of agents based on their availability. We prove that the resulting algorithm is stable in the mean-square error sense and provide a tight analysis of its Mean-Square-Deviation (MSD) performance. Various numerical experiments are conducted to illustrate our theoretical findings.