# A comprehensive review of federated learning: Methods, applications, and challenges in privacy-preserving collaborative model training

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/a-comprehensive-review-of-federated-learning-methods-applications-and-challenges/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Meenakshi Aggarwal,Vikas Khullar,Nitin Goyal |
| Citations | 22 |
| DOI | 10.1201/9781003471059-73 |
| Fields | Computer Science,Medicine,Social Sciences |
| Open Access | true |
| OA Status | gold |
| OA URL | https://api.taylorfrancis.com/content/chapters/oa-edit/download?identifierName=doi&identifierValue=10.1201/9781003471059-73&type=chapterpdf |
| OpenAlex ID | https://openalex.org/W4399583420 |
| Type | book-chapter |
| Year | 2024 |

## Paper authors

- [Meenakshi Aggarwal](https://scholariq.org/researchers/meenakshi-aggarwal/)

## Paper primary topic

- [Privacy-Preserving Technologies in Data](https://scholariq.org/topics/privacy-preserving-technologies-in-data/)

## Paper topics

- [Privacy-Preserving Technologies in Data](https://scholariq.org/topics/privacy-preserving-technologies-in-data/)
- [Privacy, Security, and Data Protection](https://scholariq.org/topics/privacy-security-and-data-protection/)
- [Artificial Intelligence in Healthcare and Education](https://scholariq.org/topics/artificial-intelligence-in-healthcare-and-education/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.