# Federated learning with hyper-parameter optimization

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/federated-learning-with-hyper-parameter-optimization/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Majid Kundroo,Taehong Kim |
| Citations | 28 |
| DOI | 10.1016/j.jksuci.2023.101740 |
| Fields | Computer Science |
| Open Access | true |
| OA Status | hybrid |
| OA URL | https://doi.org/10.1016/j.jksuci.2023.101740 |
| OpenAlex ID | https://openalex.org/W4386413375 |
| Type | article |
| Year | 2023 |

## Paper authors

- [Majid Kundroo](https://scholariq.org/researchers/majid-kundroo/)

## 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/)
- [Stochastic Gradient Optimization Techniques](https://scholariq.org/topics/stochastic-gradient-optimization-techniques/)
- [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.