Federated learning (FL) is a distributed machine learning (ML) approach that enables
models to be trained on client devices while ensuring the privacy of user data. Model …

Artificial intelligence (AI) and deep learning (DL) have shown tremendous potential in
driving sustainability across various sectors. This paper reviews recent advancements in AI …

Over the recent years, with the increasing adoption of Federated Learning (FL) algorithms
and growing concerns over personal data privacy, Privacy-Preserving Federated Learning …

Medical vision-language models (VLMs) combine computer vision (CV) and natural
language processing (NLP) to analyze visual and textual medical data. Our paper reviews …

Federated learning (FL) offers distributed machine learning on edge devices. However, the
FL model raises privacy concerns. Various techniques, such as homomorphic encryption …

Purpose Attenuation correction and scatter compensation (AC/SC) are two main steps
toward quantitative PET imaging, which remain challenging in PET-only and PET/MRI …

Federated Learning trains machine learning models on distributed devices by aggregating
local model updates instead of local data. However, privacy concerns arise as the …

Training on multiple diverse data sources is critical to ensure unbiased and generalizable
AI. In healthcare, data privacy laws prohibit data from being moved outside the country of …

Purpose The generalizability and trustworthiness of deep learning (DL)–based algorithms
depend on the size and heterogeneity of training datasets. However, because of patient …

Healthcare data are characterized by explosive growth and value, which is the private data
of patients, and its characteristics and storage environment have brought significant issues …