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 intelligence for internet of vehicles: Challenges and solutions

X Wang, H Zhu, Z Ning, L Guo… - … Surveys & Tutorials, 2023 - ieeexplore.ieee.org
With the development of communication and networking technologies, the Internet of
Vehicles (IoV) has become the foundation of smart transportation. The development of …

[HTML][HTML] Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence

S Ali, T Abuhmed, S El-Sappagh, K Muhammad… - Information fusion, 2023 - Elsevier
Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated
applications, but the outcomes of many AI models are challenging to comprehend and trust …

Towards understanding biased client selection in federated learning

YJ Cho, J Wang, G Joshi - International Conference on …, 2022 - proceedings.mlr.press
Federated learning is a distributed optimization paradigm that enables a large number of
resource-limited client nodes to cooperatively train a model without data sharing. Previous …

Towards personalized federated learning

AZ Tan, H Yu, L Cui, Q Yang - IEEE transactions on neural …, 2022 - ieeexplore.ieee.org
In parallel with the rapid adoption of artificial intelligence (AI) empowered by advances in AI
research, there has been growing awareness and concerns of data privacy. Recent …

Layer-wised model aggregation for personalized federated learning

X Ma, J Zhang, S Guo, W Xu - Proceedings of the IEEE/CVF …, 2022 - openaccess.thecvf.com
Abstract Personalized Federated Learning (pFL) not only can capture the common priors
from broad range of distributed data, but also support customized models for heterogeneous …

Client selection in federated learning: Convergence analysis and power-of-choice selection strategies

YJ Cho, J Wang, G Joshi - arXiv preprint arXiv:2010.01243, 2020 - arxiv.org
Federated learning is a distributed optimization paradigm that enables a large number of
resource-limited client nodes to cooperatively train a model without data sharing. Several …

Advances and open problems in federated learning

P Kairouz, HB McMahan, B Avent… - … and trends® in …, 2021 - nowpublishers.com
Federated learning (FL) is a machine learning setting where many clients (eg, mobile
devices or whole organizations) collaboratively train a model under the orchestration of a …

Privacy and robustness in federated learning: Attacks and defenses

L Lyu, H Yu, X Ma, C Chen, L Sun… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
As data are increasingly being stored in different silos and societies becoming more aware
of data privacy issues, the traditional centralized training of artificial intelligence (AI) models …

From distributed machine learning to federated learning: A survey

J Liu, J Huang, Y Zhou, X Li, S Ji, H Xiong… - … and Information Systems, 2022 - Springer
In recent years, data and computing resources are typically distributed in the devices of end
users, various regions or organizations. Because of laws or regulations, the distributed data …