mlsanityData to 7 Oct 2026

A Systematic Literature Review on Federated Machine Learning: From A Software Engineering Perspective

arXiv 2007.11354 ↗First posted 22 Jul 2020cs.SE cs.DC cs.LGcited by 14

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Abstract (arXiv)

Federated learning is an emerging machine learning paradigm where clients train models locally and formulate a global model based on the local model updates. To identify the state-of-the-art in federated learning and explore how to develop federated learning systems, we perform a systematic literature review from a software engineering perspective, based on 231 primary studies. Our data synthesis covers the lifecycle of federated learning system development that includes background understanding, requirement analysis, architecture design, implementation, and evaluation. We highlight and summarise the findings from the results, and identify future trends to encourage researchers to advance their current work.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Jul 2020.

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Citing papers per year: 2021: 2; 2022: 3; 2023: 6; 2024: 2; 2025: 1.

14 papers.

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  1. 2023Federated Learning for Computationally-Constrained Heterogeneous Devices: A Surveycited by 40
  2. 2023Phoenix: A Federated Generative Diffusion Modelcited by 21
  3. 2021Architectural Patterns for the Design of Federated Learning Systemscited by 17
  4. 2022Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directionscited by 14
  5. 2023The Pipeline for the Continuous Development of Artificial Intelligence Models -- Current State of Research and Practicecited by 14
  6. 2024Trustworthy Federated Learning: Privacy, Security, and Beyondcited by 11
  7. 2021FLRA: A Reference Architecture for Federated Learning Systemscited by 9
  8. 2023FederatedTrust: A Solution for Trustworthy Federated Learningcited by 5
  9. 2024Promise and Peril of Collaborative Code Generation Models: Balancing Effectiveness and Memorizationcited by 4
  10. 2025The Impact of LLM-Assistants on Software Developer Productivity: A Systematic Review and Mapping Studycited by 4
  11. 2023MLOps Spanning Whole Machine Learning Life Cycle: A Surveycited by 3
  12. 2023An investigation of licensing of datasets for machine learning based on the GQM modelcited by 1
2 more
  1. 2022Decision Models for Selecting Federated Learning Architecture Patternscited by 0
  2. 2022A Federated Learning Scheme for Neuro-developmental Disorders: Multi-Aspect ASD Detectioncited by 0