# Federated learning design and functional models: survey

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/federated-learning-design-and-functional-models-survey/

## Facts

| Field | Value |
| --- | --- |
| Author Names | John Ayeelyan,Sapdo Utomo,Adarsh Rouniyar,Hsiu-Chun Hsu,Pao‐Ann Hsiung |
| Citations | 36 |
| DOI | 10.1007/s10462-024-10969-y |
| Fields | Computer Science |
| Open Access | true |
| OA Status | hybrid |
| OA URL | https://link.springer.com/content/pdf/10.1007/s10462-024-10969-y.pdf |
| OpenAlex ID | https://openalex.org/W4404444621 |
| Type | article |
| Year | 2024 |

## Paper authors

- [Pao‐Ann Hsiung](https://scholariq.org/researchers/pao-ann-hsiung/)

## Paper journal

- [Artificial Intelligence Review](https://scholariq.org/journals/artificial-intelligence-review/)

## 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/)
- [Cryptography and Data Security](https://scholariq.org/topics/cryptography-and-data-security/)
- [Mobile Crowdsensing and Crowdsourcing](https://scholariq.org/topics/mobile-crowdsensing-and-crowdsourcing/)

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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.