mlsanityData to 7 Oct 2026

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

arXiv 2405.00556 ↗First posted 1 May 2024cs.LGcited by 2

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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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 24 of its 139 references that are in the map. First posted on arXiv 1 May 2024.

Builds on

24 of 139 references.

References · 24

  1. 2019Local Model Poisoning Attacks to Byzantine-Robust Federated Learningcited by 479
  2. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 137
  3. 2020When Federated Learning Meets Blockchain: A New Distributed Learning Paradigmcited by 31
  4. 2024Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defensescited by 30
  5. 2024Decentralized Federated Learning: A Survey on Security and Privacycited by 25
  6. 2022FEDIC: Federated Learning on Non-IID and Long-Tailed Data via Calibrated Distillationcited by 19
  7. 2021Decentralized Deep Learning for Multi-Access Edge Computing: A Survey on Communication Efficiency and Trustworthinesscited by 15
  8. 2023Fairness and Privacy-Preserving in Federated Learning: A Surveycited by 12
  9. 2022Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graphcited by 11
  10. 2021On the Fairness of Swarm Learning in Skin Lesion Classificationcited by 9
  11. 2023Backdoor Attacks in Peer-to-Peer Federated Learningcited by 9
  12. 2023Defending Against Poisoning Attacks in Federated Learning with Blockchaincited by 8
12 more
  1. 2024Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learningcited by 8
  2. 2022Demystifying Swarm Learning: A New Paradigm of Blockchain-based Decentralized Federated Learningcited by 7
  3. 2021FLoBC: A Decentralized Blockchain-Based Federated Learning Frameworkcited by 5
  4. 2021A Credibility-aware Swarm-Federated Deep Learning Framework in Internet of Vehiclescited by 4
  5. 2022CB-DSL: Communication-efficient and Byzantine-robust Distributed Swarm Learning on Non-i.i.d. Datacited by 4
  6. 2023Recent Advances in the Internet of Medical Things (IoMT) Systems Securitycited by 4
  7. 2022Integrating Human-in-the-loop into Swarm Learning for Decentralized Fake News Detectioncited by 3
  8. 2023Privacy Inference-Empowered Stealthy Backdoor Attack on Federated Learning under Non-IID Scenarioscited by 3
  9. 2022Generative Data Augmentation for Non-IID Problem in Decentralized Clinical Machine Learningcited by 2
  10. 2023A general-purpose AI assistant embedded in an open-source radiology information systemcited by 2
  11. 2023FLCC: Efficient Distributed Federated Learning on IoMT over CSMA/CAcited by 1
  12. 2024Eclipse Attack Detection on a Blockchain Network as a Non-Parametric Change Detection Problemcited by 1

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Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024Defending Against Diverse Attacks in Federated Learning Through Consensus-Based Bi-Level Optimizationcited by 1
  2. 2025VGS-ATD: Robust Distributed Learning for Multi-Label Medical Image Classification Under Heterogeneous and Imbalanced Conditionscited by 0