Preserving Accuracy in Federated Learning via Equitable Model and Efficient Aggregation

Machine learning has revolutionized research by extracting complicated patterns from complex data, particularly in healthcare and medical imaging, where accurate diagnosis is critical. The concept of federated learning has gained popularity in the field of machine learning as a viable technique for addressing privacy issues in distributed settings. This research explores federated learning in healthcare, demonstrating its capability to achieve results comparable to centralized data while enhancing the accuracy of deep learning models for clinical data interpretation. To ensure reliable model performance during federated learning rounds, this study introduces a proactive mechanism for coordinating server updates with equitable client modifications. The equitable model, designed to reduce accuracy fluctuations, consistently improves accuracy across multiple training rounds on a non-IID dataset. We achieved smooth accuracy improvement by implementing the novel Equitable model, resulting in robust model development.
As healthcare AI continues to advance, federated learning emerges as a critical tool for developing precise prediction models while preserving patient data privacy and aligning with increasingly strict data standards worldwide, such as GDPR regulations. This strategic approach not only promotes ethical, efficient, and secure progress in medical research and practice, but it also emphasizes the importance of protecting patient data privacy while utilizing machine learning's potential.