Personalized Federated

Personalized federated learning aims to train customized machine learning models for individual clients while preserving data privacy, addressing the heterogeneity inherent in federated learning settings. Current research focuses on developing algorithms that handle diverse client capabilities and data distributions, employing techniques like adaptive knowledge matching, personalized network pruning, and gradient normalization within federated frameworks. These advancements improve model accuracy and efficiency across various tasks, including multi-task learning and reinforcement learning, with applications ranging from mobile edge computing to personalized recommendations. The resulting improvements in efficiency and privacy are significant for both theoretical understanding and practical deployment of federated learning systems.