Federated Learning (FL) is a machine learning paradigm where many clients collaboratively
learn a shared global model with decentralized training data. Personalized FL additionally …

In this work, besides improving prediction accuracy, we study whether personalization could
bring robustness benefits to backdoor attacks. We conduct the first study of backdoor attacks …

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) is a way for machines to learn from data that is kept locally, in order
to protect the privacy of clients. This is typically done using local SGD, which helps to …

Federated Learning (FL) is an increasingly popular form of distributed machine learning that
addresses privacy concerns by allowing participants to collaboratively train machine …

In many real-world situations, data is distributed across multiple self-interested agents.
These agents can collaborate to build a machine learning model based on data from …

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

Cross-silo federated learning (FL) enables the development of machine learning models on
datasets distributed across data centers such as hospitals and clinical research laboratories …

Federated learning is a distributed learning framework that takes full advantage of private
data samples kept on edge devices. In real-world federated learning systems, these data …

Amid the ongoing advancements in Federated Learning (FL), a machine learning paradigm
that allows collaborative learning with data privacy protection, personalized FL (pFL) has …