Federated learning (FL) is a privacy-preserving distributed learning approach that allows
multiple parties to jointly build machine learning models without disclosing sensitive data …

As societal concerns on data privacy recently increase, we have witnessed data silos among
multiple parties in various applications. Federated learning emerges as a new learning …

Vertical Federated Learning (VFL) enables multiple parties to collaboratively train a machine
learning model over vertically distributed datasets without data privacy leakage. However …

Federated learning (FL) has emerged as a highly effective paradigm for privacy-preserving
collaborative training among different parties. Unlike traditional centralized learning, which …

Federated learning (FL) enables distributed resource-constrained devices to jointly train
shared models while keeping the training data local for privacy purposes. Vertical FL (VFL) …

Federated Learning (FL) is an emerging technique for collaboratively training machine
learning models on distributed data under privacy constraints. However, recent studies have …

Federated learning (FL) has been proposed to allow collaborative training of machine
learning (ML) models among multiple parties to keep their data private and only model …

Federated learning (FL) provides a privacy-preserving solution for distributed machine
learning tasks. One challenging problem that severely damages the performance of FL …

Federated learning is a novel distribute machine learning paradigm to support cooperative
model training among multiple participant clients, where each client keeps its private data …

Federated learning (FL) is an important technique for learning models from decentralized
data in a privacy-preserving way. Existing FL methods usually uniformly sample clients for …