mlsanityData to 7 Oct 2026

Federated learning: Applications, challenges and future directions

arXiv 2205.09513 ↗First posted 18 May 2022cs.LG cs.AI cs.ITcited by 11

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Abstract (arXiv)

Federated learning (FL) is a system in which a central aggregator coordinates the efforts of multiple clients to solve machine learning problems. This setting allows training data to be dispersed in order to protect privacy. The purpose of this paper is to provide an overview of FL systems with a focus on healthcare. FL is evaluated here based on its frameworks, architectures, and applications. It is shown here that FL solves the preceding issues with a shared global deep learning (DL) model via a central aggregator server. This paper examines recent developments and provides a comprehensive list of unresolved issues, inspired by the rapid growth of FL research. In the context of FL, several privacy methods are described, including secure multiparty computation, homomorphic encryption, differential privacy, and stochastic gradient descent. Furthermore, a review of various FL classes, such as horizontal and vertical FL and federated transfer learning, is provided. FL has applications in wireless communication, service recommendation, intelligent medical diagnosis systems, and healthcare, all of which are discussed in this paper. We also present a thorough review of existing FL challenges, such as privacy protection, communication cost, system heterogeneity, and unreliable model upload, followed by future research directions.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 57 of its 155 references that are in the map. First posted on arXiv 18 May 2022.

Builds on

57 of 155 references.

References · 57

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  2. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  3. 2018Federated Learning with Non-IID Datacited by 1,099
  4. 2019Towards Federated Learning at Scale: System Designcited by 826
  5. 2018LEAF: A Benchmark for Federated Settingscited by 780
  6. 2018How To Backdoor Federated Learningcited by 745
  7. 2017Federated Multi-Task Learningcited by 669
  8. 2020The Future of Digital Health with Federated Learningcited by 625
  9. 2020Personalized Federated Learning with Moreau Envelopescited by 612
  10. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  11. 2017Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Trainingcited by 471
  12. 2017Differentially Private Federated Learning: A Client Level Perspectivecited by 451
38 more
  1. 2019Agnostic Federated Learningcited by 451
  2. 2019Federated Machine Learning: Concept and Applicationscited by 409
  3. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 391
  4. 2019Fair Resource Allocation in Federated Learningcited by 389
  5. 2018Fairness Without Demographics in Repeated Loss Minimizationcited by 363
  6. 2017Deep Models Under the GAN: Information Leakage from Collaborative Deep Learningcited by 326
  7. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
  8. 2018Distributed learning of deep neural network over multiple agentscited by 277
  9. 2019FedHealth: A Federated Transfer Learning Framework for Wearable Healthcarecited by 182
  10. 2019Federated Learning over Wireless Fading Channelscited by 178
  11. 2017Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryptioncited by 174
  12. 2017Adaptive Neural Networks for Efficient Inferencecited by 161
  13. 2018cpSGD: Communication-efficient and differentially-private distributed SGDcited by 157
  14. 2017A Survey on Homomorphic Encryption Schemes: Theory and Implementationcited by 154
  15. 2019Federated Learning for Wireless Communications: Motivation, Opportunities and Challengescited by 128
  16. 2019Hierarchical Federated Learning Across Heterogeneous Cellular Networkscited by 98
  17. 2019Reliable Federated Learning for Mobile Networkscited by 92
  18. 2019Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networkscited by 79
  19. 2019Patient Clustering Improves Efficiency of Federated Machine Learning to predict mortality and hospital stay time using distributed Electronic Medical Recordscited by 70
  20. 2020Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imagingcited by 56
  21. 2019On Safeguarding Privacy and Security in the Framework of Federated Learningcited by 55
  22. 2018Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Datacited by 46
  23. 2018Blind Justice: Fairness with Encrypted Sensitive Attributescited by 41
  24. 2019Semi-Cyclic Stochastic Gradient Descentcited by 40
  25. 2019Differentially Private Meta-Learningcited by 40
  26. 2020Dynamic Fusion based Federated Learning for COVID-19 Detectioncited by 36
  27. 2019Robust Federated Learning with Noisy Communicationcited by 33
  28. 2018Entity Resolution and Federated Learning get a Federated Resolutioncited by 23
  29. 2020Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japancited by 23
  30. 2020A Federated Learning Framework for Healthcare IoT devicescited by 22
  31. 2017Federated Tensor Factorization for Computational Phenotypingcited by 20
  32. 2020Device Heterogeneity in Federated Learning: A Superquantile Approachcited by 20
  33. 2019Two-stage Federated Phenotyping and Patient Representation Learningcited by 19
  34. 2019Federated Uncertainty-Aware Learning for Distributed Hospital EHR Datacited by 17
  35. 2019Federated and Differentially Private Learning for Electronic Health Recordscited by 13
  36. 2020MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selectioncited by 13
  37. 2020Beyond Individual and Group Fairnesscited by 13
  38. 2019Preserving Patient Privacy while Training a Predictive Model of In-hospital Mortalitycited by 7

50 most cited of 57 All 57 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 2; 2025: 4; 2026: 1.

11 papers.

  1. 2023Transparency and Privacy: The Role of Explainable AI and Federated Learning in Financial Fraud Detectioncited by 13
  2. 2024Real-World Federated Learning in Radiology: Hurdles to overcome and Benefits to gaincited by 5
  3. 2023The Significance of Machine Learning in Clinical Disease Diagnosis: A Reviewcited by 4
  4. 2022Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directionscited by 2
  5. 2025Decentor-V: Lightweight ML Training on Low-Power RISC-V Edge Devicescited by 1
  6. 2026Scalability and Performance Evaluation of Federated Learning Frameworks: A Comparative Analysiscited by 1
  7. 2023Federated Learning on Patient Data for Privacy-Protecting Polycystic Ovary Syndrome Treatmentcited by 0
  8. 2024Towards Robust Federated Image Classification: An Empirical Study of Weight Selection Strategies in Manufacturingcited by 0
  9. 2025Secure Visual Data Processing via Federated Learningcited by 0
  10. 2025FedCCL: Federated Clustered Continual Learning Framework for Privacy-focused Energy Forecastingcited by 0
  11. 2025From Privacy to Trust in the Agentic Era: A Taxonomy of Challenges in Trustworthy Federated Learning Through the Lens of Trust Report 2.0cited by 0