  In the healthcare domain, Magnetic Resonance Imaging (MRI) assumes a pivotal
role, as it employs Artificial Intelligence (AI) and Machine Learning (ML)
methodologies to extract invaluable insights from imaging data. Nonetheless,
the imperative need for patient privacy poses significant challenges when
collecting data from diverse healthcare sources. Consequently, the Deep
Learning (DL) communities occasionally face difficulties detecting rare
features. In this research endeavor, we introduce the Ensemble-Based Federated
Learning (EBFL) Framework, an innovative solution tailored to address this
challenge. The EBFL framework deviates from the conventional approach by
emphasizing model features over sharing sensitive patient data. This unique
methodology fosters a collaborative and privacy-conscious environment for
healthcare institutions, empowering them to harness the capabilities of a
centralized server for model refinement while upholding the utmost data privacy
standards.Conversely, a robust ensemble architecture boasts potent feature
extraction capabilities, distinguishing itself from a single DL model. This
quality makes it remarkably dependable for MRI analysis. By harnessing our
groundbreaking EBFL methodology, we have achieved remarkable precision in the
classification of brain tumors, including glioma, meningioma, pituitary, and
non-tumor instances, attaining a precision rate of 94% for the Global model and
an impressive 96% for the Ensemble model. Our models underwent rigorous
evaluation using conventional performance metrics such as Accuracy, Precision,
Recall, and F1 Score. Integrating DL within the Federated Learning (FL)
framework has yielded a methodology that offers precise and dependable
diagnostics for detecting brain tumors.
