  This paper presents a comparative study of sampling methods within the
FedHome framework, designed for personalized in-home health monitoring. FedHome
leverages federated learning (FL) and generative convolutional autoencoders
(GCAE) to train models on decentralized edge devices while prioritizing data
privacy. A notable challenge in this domain is the class imbalance in health
data, where critical events such as falls are underrepresented, adversely
affecting model performance. To address this, the research evaluates six
oversampling techniques using Stratified K-fold cross-validation: SMOTE,
Borderline-SMOTE, Random OverSampler, SMOTE-Tomek, SVM-SMOTE, and SMOTE-ENN.
These methods are tested on FedHome's public implementation over 200 training
rounds with and without stratified K-fold cross-validation. The findings
indicate that SMOTE-ENN achieves the most consistent test accuracy, with a
standard deviation range of 0.0167-0.0176, demonstrating stable performance
compared to other samplers. In contrast, SMOTE and SVM-SMOTE exhibit higher
variability in performance, as reflected by their wider standard deviation
ranges of 0.0157-0.0180 and 0.0155-0.0180, respectively. Similarly, the Random
OverSampler method shows a significant deviation range of 0.0155-0.0176.
SMOTE-Tomek, with a deviation range of 0.0160-0.0175, also shows greater
stability but not as much as SMOTE-ENN. This finding highlights the potential
of SMOTE-ENN to enhance the reliability and accuracy of personalized health
monitoring systems within the FedHome framework.
