Model Personalization

Model personalization in machine learning aims to tailor models to individual users or devices, improving performance and user experience while addressing data heterogeneity and privacy concerns in federated learning settings. Current research focuses on efficient personalization techniques, such as using lightweight adapter modules, selective knowledge sharing, and hypernetworks, often applied to large language models and graph neural networks. These advancements are significant for improving the accuracy and efficiency of various applications, from personalized recommendations and medical image analysis to natural language processing and malware detection, particularly in scenarios with limited resources or privacy constraints.