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 …

Unleashing the power of edge-cloud generative ai in mobile networks: A survey of aigc services

M Xu, H Du, D Niyato, J Kang, Z Xiong… - … Surveys & Tutorials, 2024 - ieeexplore.ieee.org
Artificial Intelligence-Generated Content (AIGC) is an automated method for generating,
manipulating, and modifying valuable and diverse data using AI algorithms creatively. This …

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 …

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 …

Blockchain-empowered federated learning: Challenges, solutions, and future directions

J Zhu, J Cao, D Saxena, S Jiang, H Ferradi - ACM Computing Surveys, 2023 - dl.acm.org
Federated learning is a privacy-preserving machine learning technique that trains models
across multiple devices holding local data samples without exchanging them. There are …

A survey on federated learning: challenges and applications

J Wen, Z Zhang, Y Lan, Z Cui, J Cai… - International Journal of …, 2023 - Springer
Federated learning (FL) is a secure distributed machine learning paradigm that addresses
the issue of data silos in building a joint model. Its unique distributed training mode and the …

Federated learning: A survey on enabling technologies, protocols, and applications

M Aledhari, R Razzak, RM Parizi, F Saeed - IEEE Access, 2020 - ieeexplore.ieee.org
This paper provides a comprehensive study of Federated Learning (FL) with an emphasis
on enabling software and hardware platforms, protocols, real-life applications and use …

Privacy-preserving Byzantine-robust federated learning via blockchain systems

Y Miao, Z Liu, H Li, KKR Choo… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Federated learning enables clients to train a machine learning model jointly without sharing
their local data. However, due to the centrality of federated learning framework and the …

Lead federated neuromorphic learning for wireless edge artificial intelligence

H Yang, KY Lam, L Xiao, Z Xiong, H Hu… - Nature …, 2022 - nature.com
In order to realize the full potential of wireless edge artificial intelligence (AI), very large and
diverse datasets will often be required for energy-demanding model training on resource …

Swarm learning for decentralized artificial intelligence in cancer histopathology

OL Saldanha, P Quirke, NP West, JA James… - Nature medicine, 2022 - nature.com
Artificial intelligence (AI) can predict the presence of molecular alterations directly from
routine histopathology slides. However, training robust AI systems requires large datasets …