Centralized Model

Centralized models, a core component of traditional machine learning, are facing increasing scrutiny due to privacy concerns and scalability limitations. Current research focuses on decentralized alternatives, such as federated learning, which allows collaborative model training without directly sharing sensitive data, often employing algorithms like FedAvg or variations based on gradient descent and employing blockchain technologies for enhanced security and transparency. This shift towards decentralized approaches is significant because it addresses critical issues of data security and enables broader application of machine learning in sensitive domains like healthcare and autonomous systems while improving model robustness and efficiency.