Heterogeneous federated learning: State-of-the-art and research challenges
Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …
scale industrial applications. Existing FL works mainly focus on model homogeneous …
Sociotechnical safeguards for genomic data privacy
Recent developments in a variety of sectors, including health care, research and the direct-
to-consumer industry, have led to a dramatic increase in the amount of genomic data that …
to-consumer industry, have led to a dramatic increase in the amount of genomic data that …
Lead federated neuromorphic learning for wireless edge artificial intelligence
In order to realize the full potential of wireless edge artificial intelligence (AI), very large and
diverse datasets will often be required for energy-demanding model training on resource …
diverse datasets will often be required for energy-demanding model training on resource …
GA4GH: International policies and standards for data sharing across genomic research and healthcare
Summary The Global Alliance for Genomics and Health (GA4GH) aims to accelerate
biomedical advances by enabling the responsible sharing of clinical and genomic data …
biomedical advances by enabling the responsible sharing of clinical and genomic data …
A systematic review of homomorphic encryption and its contributions in healthcare industry
K Munjal, R Bhatia - Complex & Intelligent Systems, 2023 - Springer
Cloud computing and cloud storage have contributed to a big shift in data processing and its
use. Availability and accessibility of resources with the reduction of substantial work is one of …
use. Availability and accessibility of resources with the reduction of substantial work is one of …
Robust heterogeneous federated learning under data corruption
Abstract Model heterogeneous federated learning is a realistic and challenging problem.
However, due to the limitations of data collection, storage, and transmission conditions, as …
However, due to the limitations of data collection, storage, and transmission conditions, as …
Federated learning for medical applications: A taxonomy, current trends, challenges, and future research directions
With the advent of the Internet of Things (IoT), artificial intelligence (AI), machine learning
(ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications …
(ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications …
Decentralized federated learning: A survey and perspective
Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …
A review of Blockchain-based secure sharing of healthcare data
Medical data contains multiple records of patient data that are important for subsequent
treatment and future research. However, it needs to be stored and shared securely to protect …
treatment and future research. However, it needs to be stored and shared securely to protect …
Collaborative privacy-preserving analysis of oncological data using multiparty homomorphic encryption
Real-world healthcare data sharing is instrumental in constructing broader-based and larger
clinical datasets that may improve clinical decision-making research and outcomes …
clinical datasets that may improve clinical decision-making research and outcomes …