Abstract Personalized Federated Learning (PFL) represents a promising solution for
decentralized learning in heterogeneous data environments. Partial model personalization …

Personalized federated learning (PFL) has been widely investigated to address the
challenge of data heterogeneity, especially when a single generic model is inadequate in …

Existing personalized federated learning frameworks fail to significantly improve the
personalization of user preference learning in next Point-Of-Interest (POI) recommendations …

Prompt learning for pre-trained Vision-Language Models (VLMs) like CLIP has
demonstrated potent applicability across diverse downstream tasks. This lightweight …

Federated learning (FL) is a popular privacy-preserving paradigm that enables distributed
clients to collaboratively train models with a central server while keeping raw data locally. In …

Federated learning (FL) is an emerging distributed machine learning paradigm that enables
collaborative training of machine learning models over decentralized devices without …

Traditional federated learning has shown mediocre performance on heterogeneous data,
thus sparking increasing interest in personalized federated learning. Unlike traditional …

Federated learning (FL) is an emerging distributed machine learning paradigm enabling
collaborative model training on decentralized devices without exposing their local data. A …