# Timely Communication in Federated Learning

arXiv: https://arxiv.org/abs/2012.15831 · HTML: https://mlsanity.com/paper/2012.15831/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2020.

## Built on by (most cited first, 9 of 9)

- [On the Convergence Time of Federated Learning Over Wireless Networks Under Imperfect CSI](https://mlsanity.com/paper/2104.00331/) (2021)
- [Delay Sensitive Hierarchical Federated Learning with Stochastic Local Updates](https://mlsanity.com/paper/2302.04851/) (2023)
- [Value of Information and Timing-aware Scheduling for Federated Learning](https://mlsanity.com/paper/2312.10512/) (2023)
- [Federated learning and next generation wireless communications: A survey on bidirectional relationship](https://mlsanity.com/paper/2110.07649/) (2021)
- [Age Aware Scheduling for Differentially-Private Federated Learning](https://mlsanity.com/paper/2405.05962/) (2024)
- [Efficient Federated Learning with Timely Update Dissemination](https://mlsanity.com/paper/2507.06031/) (2025)
- [rAge-k: Communication-Efficient Federated Learning Using Age Factor](https://mlsanity.com/paper/2410.22192/) (2024)
- [CAFe: Cost and Age aware Federated Learning](https://mlsanity.com/paper/2405.15744/) (2024)
- [PPFL-RDSN: Privacy-Preserving Federated Learning-based Residual Dense Spatial Networks for Encrypted Lossy Image Reconstruction](https://mlsanity.com/paper/2507.00230/) (2025)

## Abstract (arXiv)

We consider a federated learning framework in which a parameter server (PS) trains a global model by using n clients without actually storing the client data centrally at a cloud server. Focusing on a setting where the client datasets are fast changing and highly temporal in nature, we investigate the timeliness of model updates and propose a novel timely communication scheme. Under the proposed scheme, at each iteration, the PS waits for m available clients and sends them the current model. Then, the PS uses the local updates of the earliest k out of m clients to update the global model at each iteration. We find the average age of information experienced by each client and numerically characterize the age-optimal m and k values for a given n. Our results indicate that, in addition to ensuring timeliness, the proposed communication scheme results in significantly smaller average iteration times compared to random client selection without hurting the convergence of the global learning task.