Federated learning: Applications, challenges and future directions
Federated learning (FL) refers to a system in which a central aggregator coordinates the
efforts of several clients to solve the issues of machine learning. This setting allows the …
efforts of several clients to solve the issues of machine learning. This setting allows the …
A comprehensive survey on federated learning techniques for healthcare informatics
K Dasaradharami Reddy… - Computational …, 2023 - Wiley Online Library
Healthcare is predominantly regarded as a crucial consideration in promoting the general
physical and mental health and well‐being of people around the world. The amount of data …
physical and mental health and well‐being of people around the world. The amount of data …
Federated learning-based AI approaches in smart healthcare: concepts, taxonomies, challenges and open issues
Abstract Federated Learning (FL), Artificial Intelligence (AI), and Explainable Artificial
Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare …
Intelligence (XAI) are the most trending and exciting technology in the intelligent healthcare …
Federated learning for healthcare informatics
With the rapid development of computer software and hardware technologies, more and
more healthcare data are becoming readily available from clinical institutions, patients …
more healthcare data are becoming readily available from clinical institutions, patients …
Comparing machine learning algorithms for predicting COVID-19 mortality
K Moulaei, M Shanbehzadeh… - BMC medical informatics …, 2022 - Springer
Background The coronavirus disease (COVID-19) hospitalized patients are always at risk of
death. Machine learning (ML) algorithms can be used as a potential solution for predicting …
death. Machine learning (ML) algorithms can be used as a potential solution for predicting …
Collaborative federated learning for healthcare: Multi-modal covid-19 diagnosis at the edge
Despite significant improvements over the last few years, cloud-based healthcare
applications continue to suffer from poor adoption due to their limitations in meeting stringent …
applications continue to suffer from poor adoption due to their limitations in meeting stringent …
Toward trustworthy ai: Blockchain-based architecture design for accountability and fairness of federated learning systems
Federated learning is an emerging privacy-preserving AI technique where clients (ie,
organizations or devices) train models locally and formulate a global model based on the …
organizations or devices) train models locally and formulate a global model based on the …
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 …
On privacy and personalization in cross-silo federated learning
While the application of differential privacy (DP) has been well-studied in cross-device
federated learning (FL), there is a lack of work considering DP and its implications for cross …
federated learning (FL), there is a lack of work considering DP and its implications for cross …
Privacy-first health research with federated learning
Privacy protection is paramount in conducting health research. However, studies often rely
on data stored in a centralized repository, where analysis is done with full access to the …
on data stored in a centralized repository, where analysis is done with full access to the …