# Fedstellar: A Platform for Decentralized Federated Learning

arXiv: https://arxiv.org/abs/2306.09750 · HTML: https://mlsanity.com/paper/2306.09750/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 43 references that are in the map.
- First posted on arXiv 16 Jun 2023.

## Builds on (most cited first, 20 of 20)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates](https://mlsanity.com/paper/1803.01498/) (2018)
- [Robust Aggregation for Federated Learning](https://mlsanity.com/paper/1912.13445/) (2019)
- [FedML: A Research Library and Benchmark for Federated Machine Learning](https://mlsanity.com/paper/2007.13518/) (2020)
- [Challenges in Deploying Machine Learning: a Survey of Case Studies](https://mlsanity.com/paper/2011.09926/) (2020)
- [Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges](https://mlsanity.com/paper/2211.08413/) (2022)
- [BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning](https://mlsanity.com/paper/1905.06731/) (2019)
- [Decentralized Federated Learning: Balancing Communication and Computing Costs](https://mlsanity.com/paper/2107.12048/) (2021)
- [A Survey on Device Behavior Fingerprinting: Data Sources, Techniques, Application Scenarios, and Datasets](https://mlsanity.com/paper/2008.03343/) (2020)
- [Electrosense: Open and Big Spectrum Data](https://mlsanity.com/paper/1703.09989/) (2017)
- [Decentralized Event-Triggered Federated Learning with Heterogeneous Communication Thresholds](https://mlsanity.com/paper/2204.03726/) (2022)
- [Robustness and Personalization in Federated Learning: A Unified Approach via Regularization](https://mlsanity.com/paper/2009.06303/) (2020)
- [Jointly Learning from Decentralized (Federated) and Centralized Data to Mitigate Distribution Shift](https://mlsanity.com/paper/2111.12150/) (2021)
- [Experimenting with Emerging RISC-V Systems for Decentralised Machine Learning](https://mlsanity.com/paper/2302.07946/) (2023)
- [P4L: Privacy Preserving Peer-to-Peer Learning for Infrastructureless Setups](https://mlsanity.com/paper/2302.13438/) (2023)
- [Model-Agnostic Federated Learning](https://mlsanity.com/paper/2303.04906/) (2023)
- [Scatterbrained: A flexible and expandable pattern for decentralized machine learning](https://mlsanity.com/paper/2112.07718/) (2021)
- [CyberSpec: Intelligent Behavioral Fingerprinting to Detect Attacks on Crowdsensing Spectrum Sensors](https://mlsanity.com/paper/2201.05410/) (2022)

## Built on by (most cited first, 14 of 14)

- [Advances in Robust Federated Learning: A Survey with Heterogeneity Considerations](https://mlsanity.com/paper/2405.09839/) (2024)
- [Sentinel: An Aggregation Function to Secure Decentralized Federated Learning](https://mlsanity.com/paper/2310.08097/) (2023)
- [PQS-BFL: A Post-Quantum Secure Blockchain-based Federated Learning Framework](https://mlsanity.com/paper/2505.01866/) (2025)
- [Blockchain Meets LLMs: A Living Survey on Bidirectional Integration](https://mlsanity.com/paper/2411.16809/) (2024)
- [ProFe: Communication-Efficient Decentralized Federated Learning via Distillation and Prototypes](https://mlsanity.com/paper/2412.11207/) (2024)
- [Mitigating Communications Threats in Decentralized Federated Learning through Moving Target Defense](https://mlsanity.com/paper/2307.11730/) (2023)
- [Defending Against Diverse Attacks in Federated Learning Through Consensus-Based Bi-Level Optimization](https://mlsanity.com/paper/2412.02535/) (2024)
- [Demo: A Practical Testbed for Decentralized Federated Learning on Physical Edge Devices](https://mlsanity.com/paper/2505.08033/) (2025)
- [Centralized vs Decentralized Federated Learning: A trade-off performance analysis](https://mlsanity.com/paper/2605.16089/) (2026)
- [Impact of Network Topology on Byzantine Resilience in Decentralized Federated Learning](https://mlsanity.com/paper/2407.05141/) (2024)
- [S-VOTE: Similarity-based Voting for Client Selection in Decentralized Federated Learning](https://mlsanity.com/paper/2501.19279/) (2025)
- [Towards a Distributed Federated Learning Aggregation Placement using Particle Swarm Intelligence](https://mlsanity.com/paper/2504.16227/) (2025)
- [Delta Sum Learning: an approach for fast and global convergence in Gossip Learning](https://mlsanity.com/paper/2512.01549/) (2025)
- [Making Cross-Continental Federated Learning Repeatable with FLIP: a Multi-Application Study](https://mlsanity.com/paper/2609.36001/) (2026)

## Abstract (arXiv)

In 2016, Google proposed Federated Learning (FL) as a novel paradigm to train Machine Learning (ML) models across the participants of a federation while preserving data privacy. Since its birth, Centralized FL (CFL) has been the most used approach, where a central entity aggregates participants' models to create a global one. However, CFL presents limitations such as communication bottlenecks, single point of failure, and reliance on a central server. Decentralized Federated Learning (DFL) addresses these issues by enabling decentralized model aggregation and minimizing dependency on a central entity. Despite these advances, current platforms training DFL models struggle with key issues such as managing heterogeneous federation network topologies. To overcome these challenges, this paper presents Fedstellar, a platform extended from p2pfl library and designed to train FL models in a decentralized, semi-decentralized, and centralized fashion across diverse federations of physical or virtualized devices. The Fedstellar implementation encompasses a web application with an interactive graphical interface, a controller for deploying federations of nodes using physical or virtual devices, and a core deployed on each device which provides the logic needed to train, aggregate, and communicate in the network. The effectiveness of the platform has been demonstrated in two scenarios: a physical deployment involving single-board devices such as Raspberry Pis for detecting cyberattacks, and a virtualized deployment comparing various FL approaches in a controlled environment using MNIST and CIFAR-10 datasets. In both scenarios, Fedstellar demonstrated consistent performance and adaptability, achieving F1 scores of 91%, 98%, and 91.2% using DFL for detecting cyberattacks and classifying MNIST and CIFAR-10, respectively, reducing training time by 32% compared to centralized approaches.