# Swarm Learning: A Survey of Concepts, Applications, and Trends

arXiv: https://arxiv.org/abs/2405.00556 · HTML: https://mlsanity.com/paper/2405.00556/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 139 references that are in the map.
- First posted on arXiv 1 May 2024.

## Builds on (most cited first, 24 of 24)

- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges](https://mlsanity.com/paper/2211.08413/) (2022)
- [When Federated Learning Meets Blockchain: A New Distributed Learning Paradigm](https://mlsanity.com/paper/2009.09338/) (2020)
- [Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defenses](https://mlsanity.com/paper/2402.04013/) (2024)
- [Decentralized Federated Learning: A Survey on Security and Privacy](https://mlsanity.com/paper/2401.17319/) (2024)
- [FEDIC: Federated Learning on Non-IID and Long-Tailed Data via Calibrated Distillation](https://mlsanity.com/paper/2205.00172/) (2022)
- [Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthiness](https://mlsanity.com/paper/2108.03980/) (2021)
- [Fairness and Privacy-Preserving in Federated Learning: A Survey](https://mlsanity.com/paper/2306.08402/) (2023)
- [Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graph](https://mlsanity.com/paper/2210.00325/) (2022)
- [On the Fairness of Swarm Learning in Skin Lesion Classification](https://mlsanity.com/paper/2109.12176/) (2021)
- [Backdoor Attacks in Peer-to-Peer Federated Learning](https://mlsanity.com/paper/2301.09732/) (2023)
- [Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning](https://mlsanity.com/paper/2403.19178/) (2024)
- [Defending Against Poisoning Attacks in Federated Learning with Blockchain](https://mlsanity.com/paper/2307.00543/) (2023)
- [Demystifying Swarm Learning: A New Paradigm of Blockchain-based Decentralized Federated Learning](https://mlsanity.com/paper/2201.05286/) (2022)
- [FLoBC: A Decentralized Blockchain-Based Federated Learning Framework](https://mlsanity.com/paper/2112.11873/) (2021)
- [A Credibility-aware Swarm-Federated Deep Learning Framework in Internet of Vehicles](https://mlsanity.com/paper/2108.03981/) (2021)
- [CB-DSL: Communication-efficient and Byzantine-robust Distributed Swarm Learning on Non-i.i.d. Data](https://mlsanity.com/paper/2208.05578/) (2022)
- [Recent Advances in the Internet of Medical Things (IoMT) Systems Security](https://mlsanity.com/paper/2302.04439/) (2023)
- [Integrating Human-in-the-loop into Swarm Learning for Decentralized Fake News Detection](https://mlsanity.com/paper/2201.02048/) (2022)
- [Privacy Inference-Empowered Stealthy Backdoor Attack on Federated Learning under Non-IID Scenarios](https://mlsanity.com/paper/2306.08011/) (2023)
- [Generative Data Augmentation for Non-IID Problem in Decentralized Clinical Machine Learning](https://mlsanity.com/paper/2212.01109/) (2022)
- [A general-purpose AI assistant embedded in an open-source radiology information system](https://mlsanity.com/paper/2303.10338/) (2023)
- [FLCC: Efficient Distributed Federated Learning on IoMT over CSMA/CA](https://mlsanity.com/paper/2304.13549/) (2023)
- [Eclipse Attack Detection on a Blockchain Network as a Non-Parametric Change Detection Problem](https://mlsanity.com/paper/2404.00538/) (2024)

## Built on by (most cited first, 2 of 2)

- [Defending Against Diverse Attacks in Federated Learning Through Consensus-Based Bi-Level Optimization](https://mlsanity.com/paper/2412.02535/) (2024)
- [VGS-ATD: Robust Distributed Learning for Multi-Label Medical Image Classification Under Heterogeneous and Imbalanced Conditions](https://mlsanity.com/paper/2507.18657/) (2025)

## Abstract (arXiv)

Deep learning models have raised privacy and security concerns due to their reliance on large datasets on central servers. As the number of Internet of Things (IoT) devices increases, artificial intelligence (AI) will be crucial for resource management, data processing, and knowledge acquisition. To address those issues, federated learning (FL) has introduced a novel approach to building a versatile, large-scale machine learning framework that operates in a decentralized and hardware-agnostic manner. However, FL faces network bandwidth limitations and data breaches. To reduce the central dependency in FL and increase scalability, swarm learning (SL) has been proposed in collaboration with Hewlett Packard Enterprise (HPE). SL represents a decentralized machine learning framework that leverages blockchain technology for secure, scalable, and private data management. A blockchain-based network enables the exchange and aggregation of model parameters among participants, thus mitigating the risk of a single point of failure and eliminating communication bottlenecks. To the best of our knowledge, this survey is the first to introduce the principles of Swarm Learning, its architectural design, and its fields of application. In addition, it highlights numerous research avenues that require further exploration by academic and industry communities to unlock the full potential and applications of SL.