mlsanityData to 7 Oct 2026

A Comprehensive Survey on Federated Learning: Concept and Applications

arXiv 2201.09384 ↗First posted 23 Jan 2022cs.CVcited by 4

Not on the map. Why?

Abstract (arXiv)

This paper provides a comprehensive study of Federated Learning (FL) with an emphasis on components, challenges, applications and FL environment. FL can be applicable in multiple fields and domains in real-life models. in the medical system, the privacy of patients records and their medical condition is critical data, therefore collaborative learning or federated learning comes into the picture. On other hand build an intelligent system assist the medical staff without sharing the data lead into the FL concept and one of the applications that are used is a brain tumor diagnosis intelligent system based on AI methods that can efficiently work in a collaborative environment.this paper will introduce some of the applications and related work in the medical field and work under the FL concept then summarize them to introduce the main limitations of their work.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 19 of its 40 references that are in the map. First posted on arXiv 23 Jan 2022.

Builds on

19 of 40 references.

References · 19

  1. 2019Towards Federated Learning at Scale: System Designcited by 826
  2. 2017Selective Classification for Deep Neural Networkscited by 799
  3. 2021Model-Contrastive Federated Learningcited by 762
  4. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  5. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
  6. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  7. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  8. 2020Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challengescited by 138
  9. 2020Multi-site fMRI Analysis Using Privacy-preserving Federated Learning and Domain Adaptation: ABIDE Resultscited by 122
  10. 2019BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learningcited by 115
  11. 2021Federated Learning Meets Blockchain in Edge Computing: Opportunities and Challengescited by 75
  12. 2020Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19cited by 62
7 more
  1. 2020Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imagingcited by 56
  2. 2021Collaborative Federated Learning For Healthcare: Multi-Modal COVID-19 Diagnosis at the Edgecited by 56
  3. 2020Wireless Communications for Collaborative Federated Learningcited by 47
  4. 2018Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Datacited by 46
  5. 2020Federated Self-Supervised Learning of Multi-Sensor Representations for Embedded Intelligencecited by 39
  6. 2020Diabetic Retinopathy Diagnosis based on Convolutional Neural Networkcited by 1
  7. 2020The effect of the toplolgy adaptation on search performance in overlay networkcited by 1

Built on by

Cited by year

Citing papers per year: 2023: 3; 2024: 0; 2025: 1.

4 papers.

  1. 2023Towards Building the Federated GPT: Federated Instruction Tuningcited by 184
  2. 2023Privacy and Efficiency of Communications in Federated Split Learningcited by 20
  3. 2023A Practical Recipe for Federated Learning Under Statistical Heterogeneity Experimental Designcited by 5
  4. 2025Policy-Driven AI in Dataspaces: Taxonomy, Explainability, and Pathways for Compliant Innovationcited by 0