Federated SCFGWO for Secure and High-Accuracy Brain Tumor Detection Across Multi-Center MRI

Abstract

Privacy preservation and security concerns in medical imaging have led to the implementation of federated learning (FL) as an alternative to centralized machine learning approaches. This study introduces a novel Sine and Cosine Fitness Grey Wolf Optimization (SCFGWO) algorithm integrated into an FL framework to enhance the accuracy and efficiency of brain tumor detection across multi-center MRI datasets. SCFGWO addresses traditional Grey Wolf Optimization (GWO) limitations, such as early convergence and local optima, by incorporating adaptive sine–cosine strategies for improved global exploration and local exploitation. Compared to existing methods, the proposed SCFGWO-based framework achieves superior accuracy (97.2%), Dice Similarity Coefficient (0.94), and Intersection over Union (0.90) while maintaining computational efficiency (0.015s per slice). Innovative privacy-preserving methods, including differential privacy and homomorphic encryption, ensure data security across the dataset. These contributions establish SCFGWO as a robust optimization tool for federated learning applications in medical imaging, offering improved performance and enhanced data security.