Algorithmic fairness in artificial intelligence for medicine and healthcare

RJ Chen, JJ Wang, DFK Williamson, TY Chen… - Nature biomedical …, 2023 - nature.com
In healthcare, the development and deployment of insufficiently fair systems of artificial
intelligence (AI) can undermine the delivery of equitable care. Assessments of AI models …

The evolution of distributed systems for graph neural networks and their origin in graph processing and deep learning: A survey

J Vatter, R Mayer, HA Jacobsen - ACM Computing Surveys, 2023 - dl.acm.org
Graph neural networks (GNNs) are an emerging research field. This specialized deep
neural network architecture is capable of processing graph structured data and bridges the …

Federated learning review: Fundamentals, enabling technologies, and future applications

S Banabilah, M Aloqaily, E Alsayed, N Malik… - Information processing & …, 2022 - Elsevier
Federated Learning (FL) has been foundational in improving the performance of a wide
range of applications since it was first introduced by Google. Some of the most prominent …

Edge learning for B5G networks with distributed signal processing: Semantic communication, edge computing, and wireless sensing

W Xu, Z Yang, DWK Ng, M Levorato… - IEEE journal of …, 2023 - ieeexplore.ieee.org
To process and transfer large amounts of data in emerging wireless services, it has become
increasingly appealing to exploit distributed data communication and learning. Specifically …

Re-thinking data strategy and integration for artificial intelligence: concepts, opportunities, and challenges

A Aldoseri, KN Al-Khalifa, AM Hamouda - Applied Sciences, 2023 - mdpi.com
The use of artificial intelligence (AI) is becoming more prevalent across industries such as
healthcare, finance, and transportation. Artificial intelligence is based on the analysis of …

A full dive into realizing the edge-enabled metaverse: Visions, enabling technologies, and challenges

M Xu, WC Ng, WYB Lim, J Kang, Z Xiong… - … Surveys & Tutorials, 2022 - ieeexplore.ieee.org
Dubbed “the successor to the mobile Internet,” the concept of the Metaverse has grown in
popularity. While there exist lite versions of the Metaverse today, they are still far from …

Heterogeneous federated learning: State-of-the-art and research challenges

M Ye, X Fang, B Du, PC Yuen, D Tao - ACM Computing Surveys, 2023 - dl.acm.org
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 …

Transformers in medical imaging: A survey

F Shamshad, S Khan, SW Zamir, MH Khan… - Medical Image …, 2023 - Elsevier
Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …

Wind power forecasting considering data privacy protection: A federated deep reinforcement learning approach

Y Li, R Wang, Y Li, M Zhang, C Long - Applied Energy, 2023 - Elsevier
In a modern power system with an increasing proportion of renewable energy, wind power
prediction is crucial to the arrangement of power grid dispatching plans due to the volatility …

Fusing blockchain and AI with metaverse: A survey

Q Yang, Y Zhao, H Huang, Z Xiong… - IEEE Open Journal …, 2022 - ieeexplore.ieee.org
Metaverse as the latest buzzword has attracted great attention from both industry and
academia. Metaverse seamlessly integrates the real world with the virtual world and allows …