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→Final choice
summarised in Table 3.1:
{|class="wikitable"
|+ FL frameworks table comparison
! align="center" | '''Framework'''
! align="center" | '''FedML'''
|-
| align="center" | '''ML frameworks flessibility'''
| align="center" | high
| align="center" | fair
| align="center" | high
|-
| align="center" || align="center" || align="center" || align="center" || align="center" || align="center" || align="center" || align="center" || align="center" ||-| align="center" || align="center" || align="center" || align="center" || align="center" || align="center" || align="center" || align="center" || align="center" ||-| align="center" | '''License'''
| align="center" | Apache
| align="center" | Apache
| align="center" | Apache
|-
| align="center" || align="center" || align="center" || align="center" || align="center" || align="center" || align="center" || align="center" || align="center" ||-| align="center" | '''Repo rating (stars''')
| align="center" | 413
| align="center" | 5.1k
| align="center" | 3.1k
|-
| align="center" | '''Releases'''
| align="center" | Nov 23
| align="center" | Feb 18
| align="center" | Apr 30
|-
| align="center" || align="center" || align="center" || align="center" || align="center" || align="center" || align="center" || align="center" || align="center" ||-| align="center" | '''Documenta- tion Documentation and tutorials'''
| align="center" | good
| align="center" | decent
| align="center" | bad
|-
| align="center" || align="center" || align="center" || align="center" || align="center" || align="center" || align="center" || align="center" || align="center" ||-| align="center" || align="center" || align="center" || align="center" || align="center" || align="center" || align="center" || align="center" || align="center" ||-| align="center" | '''Readiness for commer- cial commercial usage'''
| align="center" | ready
| align="center" | ready
| align="center" | ready
| align="center" | not ready
|}
These two remaining frameworks are then: Flower and NVFlare. They demonstrated the potential to address the research objectives effectively and were well-aligned with the specific requirements of the FL project. Later, these two selected frameworks will be rigorously compared, examining their capabilities in handling diverse ML models, supporting various communication protocols, and accommodating heterogeneous client configurations. The comparison will delve into the frameworks’ performance, ease of integration, and potential for real-world deployment. By focusing on these two frameworks, this research aims to provide a detailed evaluation that can serve as a valuable resource for practitioners and researchers seeking to implement FL in a variety of scenarios. The selected frameworks will undergo comprehensive testing and analysis, enabling the subsequent sections to present an informed and insightful comparison, shedding light on their respective strengths and limitations.
== Frameworks in-depth comparison: Flower vs NVFlare ==