From distributed machine learning to federated learning: A survey
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 …
users, various regions or organizations. Because of laws or regulations, the distributed data …
Federated learning in smart city sensing: Challenges and opportunities
Smart Cities sensing is an emerging paradigm to facilitate the transition into smart city
services. The advent of the Internet of Things (IoT) and the widespread use of mobile …
services. The advent of the Internet of Things (IoT) and the widespread use of mobile …
[HTML][HTML] Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence
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 …
applications, but the outcomes of many AI models are challenging to comprehend and trust …
Towards personalized federated learning
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 …
research, there has been growing awareness and concerns of data privacy. Recent …
Layer-wised model aggregation for personalized federated learning
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 …
from broad range of distributed data, but also support customized models for heterogeneous …
Towards understanding biased client selection in federated learning
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 …
resource-limited client nodes to cooperatively train a model without data sharing. Previous …
Advances and open problems in federated learning
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 …
devices or whole organizations) collaboratively train a model under the orchestration of a …
Client selection in federated learning: Convergence analysis and power-of-choice selection strategies
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 …
resource-limited client nodes to cooperatively train a model without data sharing. Several …
Privacy and robustness in federated learning: Attacks and defenses
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 …
of data privacy issues, the traditional centralized training of artificial intelligence (AI) models …
Privacy-preserving blockchain-based federated learning for traffic flow prediction
Y Qi, MS Hossain, J Nie, X Li - Future Generation Computer Systems, 2021 - Elsevier
As accurate and timely traffic flow information is extremely important for traffic management,
traffic flow prediction has become a vital component of intelligent transportation systems …
traffic flow prediction has become a vital component of intelligent transportation systems …