… of IoT image sensors, the … Federated learning (FL) paradigm prevents the legacy pretraining
classification model and authentic data sharing to cloud servers by enabling federated …

… The work sets the future directions of federated learning in remote sensing. … Subsequently,
insights into crowd-sourced data will be provided, and the federated learning (FL) paradigm …

… The federated learning (FL) solution is often adopted to resolve the problems of limited … of
data in remote sensing image classification. Privacy-preserving federated learning (PPFL) is a …

… in federated learning, as well as how federated learning is used with machine learning, deep
learning, … These sensors collect the data and take reaction on that data and then adapt that …

… capabilities and uses of Federated Learning within smart cities sensing applications. This …
how the novel Federated Learning solution can be integrated into smart city sensing to solve …

… Federated Learning (FL) has emerged as a distributed collaborative AI approach that can
enable many intelligent IoT applications, by allowing for AI training at distributed IoT devices …

… We compared the results with the default setting of federated learning (D_FL). The GFL
federated learning framework1 is used in our experiments. The results are presented in Figs. 3–5…

… for distributed machine learning such as federated learning, has … We utilized the emerging
concept of clustered federated learning … For instance, in healthcare data from several sensors/…

… on remote sensing data and federated learning have two … the applications of deep learning
in remote sensing data, and … use remote sensing data to complete the federated learning …

… a new federated learning (FL)-based aerial-ground air quality sensing framework for fine-…
end-to-end learning from haze features of haze images taken by unmanned aerial vehicles (…