# The Age of Gossip in Networks

arXiv: https://arxiv.org/abs/2102.02893 · HTML: https://mlsanity.com/paper/2102.02893/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Feb 2021.

## Built on by (most cited first, 7 of 7)

- [Version age-based client scheduling policy for federated learning](https://mlsanity.com/paper/2402.05407/) (2024)
- [Distributed Mixture-of-Agents for Edge Inference with Large Language Models](https://mlsanity.com/paper/2412.21200/) (2024)
- [Scale-Robust Timely Asynchronous Decentralized Learning](https://mlsanity.com/paper/2404.19749/) (2024)
- [Diffusion Model-based Reinforcement Learning for Version Age of Information Scheduling: Average and Tail-Risk-Sensitive Control](https://mlsanity.com/paper/2601.18069/) (2026)
- [A Learning Based Scheme for Fair Timeliness in Sparse Gossip Networks](https://mlsanity.com/paper/2310.01396/) (2023)
- [Towards efficient compression and communication for prototype-based decentralized learning](https://mlsanity.com/paper/2411.09267/) (2024)
- [Feature-Based Semantics-Aware Scheduling for Energy-Harvesting Federated Learning](https://mlsanity.com/paper/2512.01983/) (2025)

## Abstract (arXiv)

A source node updates its status as a point process and also forwards its updates to a network of observer nodes. Within the network of observers, these updates are forwarded as point processes from node to node. Each node wishes its knowledge of the source to be as timely as possible. In this network, timeliness is measured by a discrete form of age of information: each status change at the source is referred to as a version and the age at a node is how many versions out of date is its most recent update from the source. This work introduces a method for evaluating the average version age at each node in the network when nodes forward updates using a memoryless gossip protocol. This method is then demonstrated by version age analysis for a collection of simple networks. For gossip on a complete graph with symmetric updating rates, it is shown that each node has average age that grows as the logarithm of the network size.