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Principles and Components of Federated Learning Architectures

arXiv 2502.05273 ↗First posted 7 Feb 2025cs.LGcited by 0

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Abstract (arXiv)

Federated Learning (FL) is a machine learning framework where multiple clients, from mobiles to enterprises, collaboratively construct a model under the orchestration of a central server but still retain the decentralized nature of the training data. This decentralized training of models offers numerous advantages, including cost savings, enhanced privacy, improved security, and compliance with legal requirements. However, for all its apparent advantages, FL is not immune to the limitations of conventional machine learning methodologies. This article provides an elaborate explanation of the inherent concepts and features found within federated learning architecture, addressing five key domains: system heterogeneity, data partitioning, machine learning models, communication protocols, and privacy techniques. This article also highlights the limitations in this domain and proposes avenues for future work. Besides, we provide a set of architectural patterns for federated learning systems, which are derived from the systematic survey of the literature. The main elements of FL, the fundamentals of Federated Learning, and a few architectural specifics will all be better understood with the aid of this research.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 58 references that are in the map. First posted on arXiv 7 Feb 2025.

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22 of 58 references.

References · 22

  1. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  2. 2019Towards Federated Learning at Scale: System Designcited by 832
  3. 2020The Future of Digital Health with Federated Learningcited by 633
  4. 2019SecureBoost: A Lossless Federated Learning Frameworkcited by 176
  5. 2019Self-Driving Cars: A Surveycited by 148
  6. 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 139
  7. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 129
  8. 2021Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challengescited by 76
  9. 2021From Distributed Machine Learning to Federated Learning: A Surveycited by 66
  10. 2021Federated Learning for Internet of Things: Applications, Challenges, and Opportunitiescited by 50
  11. 2020AI4COVID-19: AI Enabled Preliminary Diagnosis for COVID-19 from Cough Samples via an Appcited by 48
  12. 2020Multi-Participant Multi-Class Vertical Federated Learningcited by 47
10 more
  1. 2021Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systemscited by 45
  2. 2020From Federated Learning to Federated Neural Architecture Search: A Surveycited by 42
  3. 2022Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Surveycited by 31
  4. 2021Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directionscited by 22
  5. 2020Deep Learning for Biomedical Image Reconstruction: A Surveycited by 17
  6. 2021Architectural Patterns for the Design of Federated Learning Systemscited by 17
  7. 2023Active Learning on Medical Imagecited by 10
  8. 2024Distributed Federated Learning-Based Deep Learning Model for Privacy MRI Brain Tumor Detectioncited by 9
  9. 2023Generative Adversarial Networks for Data Augmentationcited by 7
  10. 2021Wireless Federated Learning (WFL) for 6G Networks -- Part I: Research Challenges and Future Trendscited by 6

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