# PeerFL: A Simulator for Peer-to-Peer Federated Learning at Scale

arXiv: https://arxiv.org/abs/2405.17839 · HTML: https://mlsanity.com/paper/2405.17839/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 28 May 2024.

## Builds on (most cited first, 10 of 10)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Ensemble Adversarial Training: Attacks and Defenses](https://mlsanity.com/paper/1705.07204/) (2017)
- [Federated Learning for Internet of Things: A Comprehensive Survey](https://mlsanity.com/paper/2104.07914/) (2021)
- [BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning](https://mlsanity.com/paper/1905.06731/) (2019)
- [Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehicles](https://mlsanity.com/paper/2102.03401/) (2021)
- [Towards Effective Clustered Federated Learning: A Peer-to-peer Framework with Adaptive Neighbor Matching](https://mlsanity.com/paper/2203.12285/) (2022)
- [On-device Federated Learning with Flower](https://mlsanity.com/paper/2104.03042/) (2021)
- [Byzantine Fault-Tolerance in Peer-to-Peer Distributed Gradient-Descent](https://mlsanity.com/paper/2101.12316/) (2021)

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

- [SimQFL: A Quantum Federated Learning Simulator with Real-Time Visualization](https://mlsanity.com/paper/2508.12477/) (2025)

## Abstract (arXiv)

This work integrates peer-to-peer federated learning tools with NS3, a widely used network simulator, to create a novel simulator designed to allow heterogeneous device experiments in federated learning. This cross-platform adaptability addresses a critical gap in existing simulation tools, enhancing the overall utility and user experience. NS3 is leveraged to simulate WiFi dynamics to facilitate federated learning experiments with participants that move around physically during training, leading to dynamic network characteristics. Our experiments showcase the simulator's efficiency in computational resource utilization at scale, with a maximum of 450 heterogeneous devices modelled as participants in federated learning. This positions it as a valuable tool for simulation-based investigations in peer-to-peer federated learning. The framework is open source and available for use and extension to the community.