Collaborative Model

Collaborative models represent a growing area of research focused on improving the efficiency and robustness of machine learning by combining the strengths of multiple models or data sources. Current research emphasizes addressing challenges like data heterogeneity in federated learning, mitigating adversarial attacks in distributed settings, and enhancing model diversity through techniques such as dual-branch architectures and self-supervised learning. These advancements are significant for improving the accuracy and reliability of AI systems across diverse applications, including image processing, personalized medicine, and secure data analysis.