Federated learning (FL) is a prevalent distributed machine learning approach that enables
collaborative training of a global model across multiple devices without sharing local data …

Vertical federated learning (VFL) provides a privacy-preserving method for machine
learning, enabling collaborative training across multiple institutions with vertically distributed …

Cued Speech (CS) is a visual coding tool to encode spoken languages at the phonetic level,
which combines lip-reading and hand gestures to effectively assist communication among …

As an emerging privacy-preserving approach to leveraging cross-platform user interactions,
vertical federated learning (VFL) has been increasingly applied in recommender systems …

Vertical Federated Learning (VFL) is a privacy-preserving collaborative learning paradigm
that enables multiple parties with distinct feature sets to jointly train machine learning …

Vertical Federated Learning (VFL) facilitates collaboration on model training among multiple
parties, each owning partitioned features of the distributed dataset. Although backdoor …

Estimating the post-click conversion rate (CVR) accurately in ranking systems is crucial in
industrial applications. However, this task is often challenged by data sparsity and selection …

Vertical Federated Learning (VFL) is a privacy-preserving distributed learning paradigm
where different parties collaboratively learn models using partitioned features of shared …

Vertical Federated Learning (VFL) is a crucial paradigm for training machine learning
models on feature-partitioned, distributed data. However, due to privacy restrictions, few …

One of the paradigms under which split learning (SL) is used is for the vertical federated
learning (VFL) setting, where two or more parties build models over feature-partitioned data …