A survey of trustworthy federated learning: Issues, solutions, and challenges
Trustworthy artificial intelligence (TAI) has proven invaluable in curbing potential negative
repercussions tied to AI applications. Within the TAI spectrum, federated learning (FL) …
repercussions tied to AI applications. Within the TAI spectrum, federated learning (FL) …
FedSDG-FS: Efficient and secure feature selection for vertical federated learning
Vertical Federated Learning (VFL) enables multiple data owners, each holding a different
subset of features about largely overlapping sets of data sample (s), to jointly train a useful …
subset of features about largely overlapping sets of data sample (s), to jointly train a useful …
[HTML][HTML] Add noise to remove noise: Local differential privacy for feature selection
Feature selection has become significantly important for data analysis. It selects the most
informative features describing the data to filter out the noise, complexity, and over-fitting …
informative features describing the data to filter out the noise, complexity, and over-fitting …
FEAST: A communication-efficient federated feature selection framework for relational data
Vertical federated learning (VFL) is an emerging paradigm for cross-silo organizations to
build more accurate machine learning (ML) models. In this setting, multiple organizations (ie …
build more accurate machine learning (ML) models. In this setting, multiple organizations (ie …
On the Gini-impurity preservation for privacy random forests
XR Xie, MJ Yuan, X Bai, W Gao… - Advances in Neural …, 2024 - proceedings.neurips.cc
Random forests have been one successful ensemble algorithms in machine learning.
Various techniques have been utilized to preserve the privacy of random forests from …
Various techniques have been utilized to preserve the privacy of random forests from …
Approximate homomorphic encryption based privacy-preserving machine learning: a survey
J Yuan, W Liu, J Shi, Q Li - Artificial Intelligence Review, 2025 - Springer
Abstract Machine Learning (ML) is rapidly advancing, enabling various applications that
improve people's work and daily lives. However, this technical progress brings privacy …
improve people's work and daily lives. However, this technical progress brings privacy …
A comprehensive analysis of privacy protection techniques developed for COVID-19 pandemic
A Majeed, SO Hwang - IEEE Access, 2021 - ieeexplore.ieee.org
Since the emergence of coronavirus disease–2019 (COVID-19) outbreak, every country has
implemented digital solutions in the form of mobile applications, web-based frameworks …
implemented digital solutions in the form of mobile applications, web-based frameworks …
Towards interpretable federated learning
Federated learning (FL) enables multiple data owners to build machine learning models
collaboratively without exposing their private local data. In order for FL to achieve …
collaboratively without exposing their private local data. In order for FL to achieve …
Secure similar image matching (sesim): An improved privacy preserving image retrieval protocol over encrypted cloud database
The emergence of cloud computing provides new dimension for the user to perform
computations and store huge amount of data say images, video, audio etc,. However, the …
computations and store huge amount of data say images, video, audio etc,. However, the …
Training differentially private models with secure multiparty computation
We address the problem of learning a machine learning model from training data that
originates at multiple data owners while providing formal privacy guarantees regarding the …
originates at multiple data owners while providing formal privacy guarantees regarding the …