# Version age-based client scheduling policy for federated learning

arXiv: https://arxiv.org/abs/2402.05407 · HTML: https://mlsanity.com/paper/2402.05407/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 24 references that are in the map.
- First posted on arXiv 8 Feb 2024.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification](https://mlsanity.com/paper/1909.06335/) (2019)
- [Scheduling Policies for Federated Learning in Wireless Networks](https://mlsanity.com/paper/1908.06287/) (2019)
- [LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning](https://mlsanity.com/paper/1805.09965/) (2018)
- [On Large-Cohort Training for Federated Learning](https://mlsanity.com/paper/2106.07820/) (2021)
- [Multi-Armed Bandit Based Client Scheduling for Federated Learning](https://mlsanity.com/paper/2007.02315/) (2020)
- [Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks](https://mlsanity.com/paper/1910.14648/) (2019)
- [Efficient Decentralized Deep Learning by Dynamic Model Averaging](https://mlsanity.com/paper/1807.03210/) (2018)
- [Taming Fat-Tailed ("Heavier-Tailed'' with Potentially Infinite Variance) Noise in Federated Learning](https://mlsanity.com/paper/2210.00690/) (2022)
- [The Age of Gossip in Networks](https://mlsanity.com/paper/2102.02893/) (2021)
- [The Cost of Delay in Status Updates and their Value: Non-linear Ageing](https://mlsanity.com/paper/1812.09320/) (2018)
- [Version Age of Information in Clustered Gossip Networks](https://mlsanity.com/paper/2109.08669/) (2021)
- [Version Age-Optimal Cached Status Updates in a Gossiping Network with Energy Harvesting Sensor](https://mlsanity.com/paper/2311.01909/) (2023)
- [Value of Information and Timing-aware Scheduling for Federated Learning](https://mlsanity.com/paper/2312.10512/) (2023)

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

- [Battery-aware Cyclic Scheduling in Energy-harvesting Federated Learning](https://mlsanity.com/paper/2504.12181/) (2025)
- [Diffusion Model-based Reinforcement Learning for Version Age of Information Scheduling: Average and Tail-Risk-Sensitive Control](https://mlsanity.com/paper/2601.18069/) (2026)
- [CAFe: Cost and Age aware Federated Learning](https://mlsanity.com/paper/2405.15744/) (2024)
- [Feature-Based Semantics-Aware Scheduling for Energy-Harvesting Federated Learning](https://mlsanity.com/paper/2512.01983/) (2025)
- [Mixed-Timescale Differential Coding for Downlink Model Broadcast in Wireless Federated Learning](https://mlsanity.com/paper/2607.13119/) (2026)

## Abstract (arXiv)

Federated Learning (FL) has emerged as a privacy-preserving machine learning paradigm facilitating collaborative training across multiple clients without sharing local data. Despite advancements in edge device capabilities, communication bottlenecks present challenges in aggregating a large number of clients; only a portion of the clients can update their parameters upon each global aggregation. This phenomenon introduces the critical challenge of stragglers in FL and the profound impact of client scheduling policies on global model convergence and stability. Existing scheduling strategies address staleness but predominantly focus on either timeliness or content. Motivated by this, we introduce the novel concept of Version Age of Information (VAoI) to FL. Unlike traditional Age of Information metrics, VAoI considers both timeliness and content staleness. Each client's version age is updated discretely, indicating the freshness of information. VAoI is incorporated into the client scheduling policy to minimize the average VAoI, mitigating the impact of outdated local updates and enhancing the stability of FL systems.