mlsanityData to 7 Oct 2026

A Systematic Literature Review on Federated Learning: From A Model Quality Perspective

arXiv 2012.01973 ↗First posted 1 Dec 2020cs.LG cs.CRcited by 15

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Abstract (arXiv)

As an emerging technique, Federated Learning (FL) can jointly train a global model with the data remaining locally, which effectively solves the problem of data privacy protection through the encryption mechanism. The clients train their local model, and the server aggregates models until convergence. In this process, the server uses an incentive mechanism to encourage clients to contribute high-quality and large-volume data to improve the global model. Although some works have applied FL to the Internet of Things (IoT), medicine, manufacturing, etc., the application of FL is still in its infancy, and many related issues need to be solved. Improving the quality of FL models is one of the current research hotspots and challenging tasks. This paper systematically reviews and objectively analyzes the approaches to improving the quality of FL models. We are also interested in the research and application trends of FL and the effect comparison between FL and non-FL because the practitioners usually worry that achieving privacy protection needs compromising learning quality. We use a systematic review method to analyze 147 latest articles related to FL. This review provides useful information and insights to both academia and practitioners from the industry. We investigate research questions about academic research and industrial application trends of FL, essential factors affecting the quality of FL models, and compare FL and non-FL algorithms in terms of learning quality. Based on our review's conclusion, we give some suggestions for improving the FL model quality. Finally, we propose an FL application framework for practitioners.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Dec 2020.

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Citing papers per year: 2021: 1; 2022: 1; 2023: 5; 2024: 2; 2025: 5; 2026: 1.

15 papers.

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  1. 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 34
  2. 2021Failure Prediction in Production Line Based on Federated Learning: An Empirical Studycited by 11
  3. 2023Federated Deep Multi-View Clustering with Global Self-Supervisioncited by 5
  4. 2023Trustworthy Federated Learning: A Surveycited by 4
  5. 2023A Systematic Literature Review on Client Selection in Federated Learningcited by 3
  6. 2023A review of federated learning in renewable energy applications: Potential, challenges, and future directionscited by 3
  7. 2024Federated Learning in Healthcare: Model Misconducts, Security, Challenges, Applications, and Future Research Directions -- A Systematic Reviewcited by 1
  8. 2025Federated Incomplete Multi-view Clustering with Globally Fused Graph Guidancecited by 1
  9. 2023Federated Learning for Energy Constrained IoT devices: A systematic mapping studycited by 0
  10. 2024Privacy-preserving federated prediction of pain intensity change based on multi-center survey datacited by 0
  11. 2025MedSegNet10: A Publicly Accessible Network Repository for Split Federated Medical Image Segmentationcited by 0
  12. 2025Federated Item Response Models: A Gradient-driven Privacy-preserving Framework for Distributed Psychometric Estimationcited by 0
3 more
  1. 2025A Service-Oriented Adaptive Hierarchical Incentive Mechanism for Federated Learningcited by 0
  2. 2025Differential Privacy in Federated Learning: Mitigating Inference Attacks with Randomized Responsecited by 0
  3. 2026FedImp: Enhancing Federated Learning Convergence with Impurity-Based Weightingcited by 0