Abstract—Federated learning constitutes a decentralized ma-chine learning methodology, enabling the training of models ondistributeddatawithoutnecessitatingcentralizedaggregation.The selection of an appropriate federated learning frameworkassumesparamountsignificanceinachievingoptimalmodelperformance. This research endeavors to conduct a comparativeassessment of various prominent federated learning frameworksusingtheFlowerframework,awidelyrecognizedbenchmarkdatasetforevaluatingfederatedlearningalgorithms.Ourfindingsreveal that the Flower Framework exhibits superior performancewithrespecttoflexibilityandcustomizationwhenjuxtaposedwithalternative frameworks. These outcomes suggest that the FlowerFramework holds promise as a judicious choice for practitionersembarking on the deployment of federated learning within thecontext of the Flower framework. In summary, this investigationunderscores the critical nature of the selection of a federatedlearning framework in the practical application of this techniquetoreal-worldchallenges