SinoXivFederated learning (FL) has emerged as a paradigm-shifting approach to distributed machine learning, enabling multiple participants to collaboratively train models without exposing raw data. However, ...
In modern manufacturing value chains, achieving optimal product quality and sustainability necessitates collaboration across interconnected stakeholders. Conventional Latent Variable Model Inversion (...
Data heterogeneity remains one of the most significant challenges in federated learning (FL), impacting model performance, convergence, and scalability. This issue is especially critical in healthcare...
Federated test-time adaptation (FTTA) enables privacy-preserving model adaptation to unlabeled target data during inference, yet it struggles with dynamic source client availability and uncertain test...
Federated learning has emerged as the dominant paradigm for privacy-preserving intrusion detection across distributed networks, yet its vulnerability to adversarial manipulation of the training proces...
The growing prevalence of diabetes highlights the need for scalable, accurate, and privacy-conscious testing technologies. To train models, traditional machine learning (ML) techniques often rely on c...
