Abstract Vision Transformers (ViT) and Visual Prompt Tuning (VPT) achieve state-of-the-art
performance with improved efficiency in various computer vision tasks. This suggests a …

Federated learning (FL) emerges as a decentralized learning framework which trains
models from multiple distributed clients without sharing their data to preserve privacy …

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

Federated learning (FL) is a powerful technology that enables collaborative training of
machine learning models without sharing private data among clients. The fundamental …

Abstract Recent developments in Artificial Intelligence techniques have enabled their
successful application across a spectrum of commercial and industrial settings. However …

Prompt learning in pretrained visual-language models has shown remarkable flexibility
across various downstream tasks. Leveraging its inherent lightweight nature recent research …

We propose a novel contrastive learning framework to effectively address the challenges of
data heterogeneity in federated learning. We first analyze the inconsistency of gradient …

Abstract Personalized Federated Learning (PFL) is primarily designed to provide
customized models for each client to better fit the non-iid distributed client data which is a …

Federated Learning (FL) aggregates locally trained models from individual clients to
construct a global model. While FL enables learning a model with data privacy it often …

Federated learning has emerged as a promising distributed machine learning paradigm to
preserve data privacy. One of the fundamental challenges of federated learning is that data …