# FL-QNNs: Memory Efficient and Privacy Preserving Framework for Peripheral Blood Cell Classification

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/fl-qnns-memory-efficient-and-privacy-preserving-framework-for-peripheral-blood/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Meenakshi Aggarwal,Vikas Khullar,Nitin Goyal,Bhavani Sankar Panda,Hardik Doshi,Nafeesh Ahmad,Vivek Bhardwaj,Gaurav Sharma |
| Citations | 3 |
| DOI | 10.1109/access.2025.3595861 |
| Fields | Computer Science |
| Open Access | true |
| OA Status | gold |
| OA URL | https://doi.org/10.1109/access.2025.3595861 |
| OpenAlex ID | https://openalex.org/W4412985031 |
| Type | article |
| Year | 2025 |

## Paper authors

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

## Paper journal

- [IEEE Access](https://scholariq.org/journals/ieee-access/)

## Paper primary topic

- [Digital Imaging for Blood Diseases](https://scholariq.org/topics/digital-imaging-for-blood-diseases/)

## Paper topics

- [Digital Imaging for Blood Diseases](https://scholariq.org/topics/digital-imaging-for-blood-diseases/)
- [AI in cancer detection](https://scholariq.org/topics/ai-in-cancer-detection/)
- [Machine Learning in Healthcare](https://scholariq.org/topics/machine-learning-in-healthcare/)

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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.