Personalized Model

Personalized models aim to tailor machine learning models to individual users or devices, addressing the limitations of generic models in handling diverse data distributions and user needs. Current research focuses on developing efficient algorithms for federated learning, where personalization occurs without centralizing sensitive data, often employing techniques like adapter modules, parameter decomposition, and meta-learning. This field is significant for improving the accuracy and fairness of AI systems across various applications, from personalized medicine and education to recommendation systems and human-robot interaction, by ensuring models effectively cater to individual characteristics.