Federated learning-based machine learning for predicting brain tumor

The swift advancements in artificial intelligence (AI) and machine learning have profoundly impacted the realm of medical research, particularly in the realm of diagnosing and treating intricate conditions such as brain tumors. These tumors, characterized by unregulated cell proliferation, pose significant challenges. The complexities inherent in brain tumor diagnosis stem from the intricate nature of these tumors, symptom overlap with other ailments, and the inherent complexity of the brain itself. Nevertheless, the application of an advanced machine learning algorithm known as Federated Learning (FL) has demonstrated its potential to address data privacy concerns and enhance diagnostic accuracy in this context. This essay discusses the application of FL which is a decentralized training strategy in brain tumor research. FL allows multiple institutions to train the model collaboratively without data sharing. The key advancement includes the improved U-Net model implementation and the utilization of Convolutional Neural Network (CNN) Ensemble Architectures for brain tumor identification. This paper also discusses the potential of FL in optimizing weight sharing for model aggregation in heterogeneous data. Furthermore, it underscores the important role of FL in modern healthcare since FL also solves the privacy concern in smart healthcare. However, challenges such as communication lag, data heterogeneity, and computational cost still exist.