Vertical Federated Learning (VFL) is a federated learning setting where multiple parties with
different features about the same set of users jointly train machine learning models without …

Federated learning is the process of developing machine learning models over datasets
distributed across data centers such as hospitals, clinical research labs, and mobile devices …

Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms
and growing concerns over personal data privacy, Privacy-Preserving Federated Learning …

Federated learning (FL) has been proposed to protect data privacy and virtually assemble
the isolated data silos by cooperatively training models among organizations without …

Differential privacy is the de-facto technique for protecting the individuals in the training
dataset and the learning models in deep learning. However, the technique presents two …

Federated Learning (FL), emerging as a distributed machine learning, is a popular paradigm
that allows multiple users to collaboratively train an intermediate model by exchanging local …

The emergence of edge computing guarantees the combination of the Internet of Things
(IoT) and artificial intelligence (AI). The vertical federated learning (VFL) framework, usually …

Most of the existing FL systems focus on a data-parallel architecture where training data are
partitioned by samples among several parties. In some real-life applications, however …

Vertical split learning is a new paradigm of federated learning for participants with vertically
partitioned data. In this paper, we make the first attempt to explore the possibility of backdoor …

Although deep cross-modal hashing methods have shown superiorities for cross-modal
retrieval recently, there is a concern about potential data privacy leakage when training the …