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 …
The Impact of Adversarial Attacks on Federated Learning: A Survey
Federated learning (FL) has emerged as a powerful machine learning technique that
enables the development of models from decentralized data sources. However, the …
enables the development of models from decentralized data sources. However, the …
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 …
Specificity-preserving federated learning for MR image reconstruction
Federated learning (FL) can be used to improve data privacy and efficiency in magnetic
resonance (MR) image reconstruction by enabling multiple institutions to collaborate without …
resonance (MR) image reconstruction by enabling multiple institutions to collaborate without …
Do gradient inversion attacks make federated learning unsafe?
Federated learning (FL) allows the collaborative training of AI models without needing to
share raw data. This capability makes it especially interesting for healthcare applications …
share raw data. This capability makes it especially interesting for healthcare applications …
Security and privacy threats to federated learning: Issues, methods, and challenges
Federated learning (FL) has nourished a promising method for data silos, which enables
multiple participants to construct a joint model collaboratively without centralizing data. The …
multiple participants to construct a joint model collaboratively without centralizing data. The …
[HTML][HTML] Encrypted federated learning for secure decentralized collaboration in cancer image analysis
Artificial intelligence (AI) has a multitude of applications in cancer research and oncology.
However, the training of AI systems is impeded by the limited availability of large datasets …
However, the training of AI systems is impeded by the limited availability of large datasets …
[HTML][HTML] The FeatureCloud platform for federated learning in biomedicine: unified approach
Background Machine learning and artificial intelligence have shown promising results in
many areas and are driven by the increasing amount of available data. However, these data …
many areas and are driven by the increasing amount of available data. However, these data …
A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources
Electronic Health Records (EHRs) are a valuable asset to facilitate clinical research and
point of care applications; however, many challenges such as data privacy concerns impede …
point of care applications; however, many challenges such as data privacy concerns impede …