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 …

Motivated by the advancing computational capacity of distributed end-user equipment (UE),
as well as the increasing concerns about sharing private data, there has been considerable …

Paddy leaf diseases encompass a range of ailments affecting rice plants' leaves, arising
from factors like bacteria, fungi, viruses, and environmental stress. Precision agriculture …

Federated Learning (FL) aims to train machine learning models for multiple clients without
sharing their own private data. Due to the heterogeneity of clients' local data distribution …

Vertical federated learning (VFL) is a promising category of federated learning for the
scenario where data is vertically partitioned and distributed among parties. VFL enriches the …

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

Abstract Vertical Federated Learning (VFL) is a collaborative machine learning paradigm
that enables multiple participants to jointly train a model on their private data without sharing …

We propose flexible vertical federated learning (Flex-VFL), a distributed machine algorithm
that trains a smooth, nonconvex function in a distributed system with vertically partitioned …

Vertical federated learning (VFL) is an emerging paradigm that allows different parties (eg,
organizations or enterprises) to collaboratively build machine learning models with privacy …

In recent years, deep learning (DL) models have demonstrated remarkable achievements
on non-trivial tasks such as speech recognition, image processing, and natural language …