The increasing availability of biomedical data from large biobanks, electronic health records,
medical imaging, wearable and ambient biosensors, and the lower cost of genome and …

Recent success of deep learning is largely attributed to the sheer amount of data used for
training deep neural networks. Despite the unprecedented success, the massive data …

摘要大数据(Big Data) 已成为学术界和产业界的研究热点, 正影响着人们日常生活,
工作习惯及思考方式. 但是目前大数据在收集, 存储和使用过程中面临着诸多安全风险 …

Federated learning is an emerging distributed machine learning framework for privacy
preservation. However, models trained in federated learning usually have worse …

Many widely used datasets for graph machine learning tasks have generally been
homophilous, where nodes with similar labels connect to each other. Recently, new Graph …

Federated learning (FL) is a new breed of Artificial Intelligence (AI) that builds upon
decentralized data and training that brings learning to the edge or directly on-device. FL is a …

The newly emerged machine learning (eg, deep learning) methods have become a strong
driving force to revolutionize a wide range of industries, such as smart healthcare, financial …

Using large, multi-national datasets for high-performance medical imaging AI systems
requires innovation in privacy-preserving machine learning so models can train on sensitive …

The broad application of artificial intelligence techniques in medicine is currently hindered
by limited dataset availability for algorithm training and validation, due to the absence of …

Natural language reflects our private lives and identities, making its privacy concerns as
broad as those of real life. Language models lack the ability to understand the context and …