A state-of-the-art survey on solving non-iid data in federated learning
Federated Learning (FL) proposed in recent years has received significant attention from
researchers in that it can enable multiple clients to cooperatively train global models without …
researchers in that it can enable multiple clients to cooperatively train global models without …
A review of medical federated learning: Applications in oncology and cancer research
A Chowdhury, H Kassem, N Padoy, R Umeton… - International MICCAI …, 2021 - Springer
Abstract Machine learning has revolutionized every facet of human life, while also becoming
more accessible and ubiquitous. Its prevalence has had a powerful impact in healthcare …
more accessible and ubiquitous. Its prevalence has had a powerful impact in healthcare …
Federated learning for generalization, robustness, fairness: A survey and benchmark
Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …
collaboration among different parties. Recently, with the popularity of federated learning, an …
Harmofl: Harmonizing local and global drifts in federated learning on heterogeneous medical images
Multiple medical institutions collaboratively training a model using federated learning (FL)
has become a promising solution for maximizing the potential of data-driven models, yet the …
has become a promising solution for maximizing the potential of data-driven models, yet the …
Data heterogeneity-robust federated learning via group client selection in industrial IoT
Nowadays, the Industrial Internet of Things (IIoT) has played an integral role in Industry 4.0
and produced massive amounts of data for industrial intelligence. These data locate on …
and produced massive amounts of data for industrial intelligence. These data locate on …
Hfedms: Heterogeneous federated learning with memorable data semantics in industrial metaverse
Federated Learning (FL), as a rapidly evolving privacy-preserving collaborative machine
learning paradigm, is a promising approach to enable edge intelligence in the emerging …
learning paradigm, is a promising approach to enable edge intelligence in the emerging …
A transfer learning approach to breast cancer classification in a federated learning framework
Artificial intelligence (AI) technologies have seen strong development. Many applications
now use AI to diagnose breast cancer. However, most new research has only been …
now use AI to diagnose breast cancer. However, most new research has only been …
Memory-aware curriculum federated learning for breast cancer classification
Abstract Background and Objective: For early breast cancer detection, regular screening
with mammography imaging is recommended. Routine examinations result in datasets with …
with mammography imaging is recommended. Routine examinations result in datasets with …
Personalized federated learning with adaptive batchnorm for healthcare
There is a growing interest in applying machine learning techniques to healthcare. Recently,
federated machine learning (FL) is gaining popularity since it allows researchers to train …
federated machine learning (FL) is gaining popularity since it allows researchers to train …
Personalized retrogress-resilient federated learning toward imbalanced medical data
Clinically oriented deep learning algorithms, combined with large-scale medical datasets,
have significantly promoted computer-aided diagnosis. To address increasing ethical and …
have significantly promoted computer-aided diagnosis. To address increasing ethical and …