Comparison of Federated Learning and Fair Federated Learning for Pneumonia Patient Classification

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Abstract

Objectives: This study aims to compare the performance of federated learning (FL) and fair federated learning (FFL) in classifying pneumonia patients based on chest X-ray data. The primary focus is on assessing the accuracy and fairness of these models in handling imbalanced and distributed data in real-world healthcare settings.Methods: We used a large chest X-ray dataset to evaluate the performance of FL and FFL models. The models were built using the ResNet50 architecture, and experiments were conducted under both independent and identically distributed (IID) and non-IID data conditions. The FFL approach applied optimized loss functions to address data imbalance and ensure fair contribution from each client, regardless of the local data distribution.Results: Our findings indicate that FFL consistently outperforms traditional FL models, particularly in non-IID environments. The FFL model demonstrated higher accuracy in pneumonia classification, achieving a significant improvement in model fairness and performance across different client datasets. The use of the ResNet50 architecture further enhanced the model’s ability to handle complex X-ray image patterns.Conclusions: FFL offers a superior solution for handling imbalanced medical data compared to conventional FL models. Its ability to maintain fairness while improving classification accuracy makes it an ideal approach for decentralized healthcare systems, ensuring better patient outcomes while preserving data privacy.