In the context of Vertical Federated Learning (VFL), agents utilize multimodal data on their
edge devices to corporately train and inference with the deep learning models. However, in …

Federated learning aims to complete model training without private data sharing, but many
privacy risks remain. Recent studies have shown that federated learning is vulnerable to …

Federated learning (FL) enables multiple clients to collaboratively build a global learning
model without sharing their own raw data for privacy protection. Unfortunately, recent …

Vertical Federated Learning (VFL) is a category of Federated Learning in which models are
trained collaboratively among parties with vertically partitioned data. Typically, in a VFL …

Federated Learning (FL) has recently made significant progress as a new machine learning
paradigm for privacy protection. Due to the high communication cost of traditional FL, one …

Vertical federated learning (vFL) has gained much attention and been deployed to solve
machine learning problems with data privacy concerns in recent years. However, some …

Federated unlearning is a promising paradigm for protecting the data ownership of
distributed clients. It allows central servers to remove historical data effects within the …

Federated learning is a decentralized learning approach that shows promise for preserving
users' privacy by avoiding local data sharing. However, the heterogeneous data in federated …

An emergence of attention and regulations on consumer privacy can be observed over the
recent years with the ubiquitous availability of IoT systems handling personal data …

In recent years, federated learning has attracted more and more attention as it could
collaboratively train a global model without gathering the users' raw data. It has brought …