Federated And Multi-Modal Learning Algorithms for Healthcare and Cross-Domain Analytics

The rapid growth of healthcare data has come with a surge in demand for privacy-preserving cross-domain analytics, hence the development of Federated Hybrid Multi-Modal Analytics (FH-MMA). This advanced framework safely and efficiently provides deep insights through multi-modal integration using federated learning techniques. FH-MMA incorporates Federative Learning for the training design architecture in a distributed manner, Convolutional Neural Networks (CNNs) for feature extraction in images, transformer models for sequential data, and Graph Neural Networks (GNNs) to model relational data. Moreover, attention mechanisms are integrated into the framework to allow cross-modal interactions, while the dynamic fusion strategy follows a late-stage feature aggregation approach based on weighted ensemble techniques. Particle Swam Optimization (PSO) fine-tunes the hyperparameters to optimize the model's performance. Experiments conducted on multi-modal healthcare datasets show that the results from FH-MMA increased diagnostic accuracy by 25%, reduced computational overhead by 30%, and showed robust scalability across domains compared to centralized and unimodal baselines. These results determine the potential of FH-MMA to make a transformational impact on personalized healthcare and cross-domain analytics via secure, adaptive, and accurate enhancements of decision-making processes.