R Yu, S Liu, X Wang - IEEE Transactions on Pattern Analysis …, 2023 - ieeexplore.ieee.org
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 …
Federated learning is an emerging distributed machine learning framework for privacy preservation. However, models trained in federated learning usually have worse …
D Lim, F Hohne, X Li, SL Huang… - Advances in …, 2021 - proceedings.neurips.cc
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 …