Combining federated learning and edge computing toward ubiquitous intelligence in 6G network: Challenges, recent advances, and future directions
Full leverage of the huge volume of data generated on a large number of user devices for
providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to …
providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to …
A survey on location and motion tracking technologies, methodologies and applications in precision sports
Sports involve commonly players and equipment of high dynamics. Their location and
motion data are essential for sports digitalization-related applications, such as from …
motion data are essential for sports digitalization-related applications, such as from …
Efficient parallel split learning over resource-constrained wireless edge networks
The increasingly deeper neural networks hinder the democratization of privacy-enhancing
distributed learning, such as federated learning (FL), to resource-constrained devices. To …
distributed learning, such as federated learning (FL), to resource-constrained devices. To …
Adaptive configuration for heterogeneous participants in decentralized federated learning
Data generated at the network edge can be processed locally by leveraging the paradigm of
edge computing (EC). Aided by EC, decentralized federated learning (DFL), which …
edge computing (EC). Aided by EC, decentralized federated learning (DFL), which …
FedFA: Federated learning with feature anchors to align features and classifiers for heterogeneous data
Federated learning allows multiple clients to collaboratively train a model without
exchanging their data, thus preserving data privacy. Unfortunately, it suffers significant …
exchanging their data, thus preserving data privacy. Unfortunately, it suffers significant …
Accelerating federated learning with data and model parallelism in edge computing
Recently, edge AI has been launched to mine and discover valuable knowledge at network
edge. Federated Learning, as an emerging technique for edge AI, has been widely …
edge. Federated Learning, as an emerging technique for edge AI, has been widely …
Vehicle selection and resource allocation for federated learning-assisted vehicular network
To exploit the massive amounts of onboard data in vehicular networks while protecting data
privacy and security, federated learning (FL) is regarded as a promising technology to …
privacy and security, federated learning (FL) is regarded as a promising technology to …
Yoga: Adaptive layer-wise model aggregation for decentralized federated learning
Traditional Federated Learning (FL) is a promising paradigm that enables massive edge
clients to collaboratively train deep neural network (DNN) models without exposing raw data …
clients to collaboratively train deep neural network (DNN) models without exposing raw data …
Bose: Block-wise federated learning in heterogeneous edge computing
At the network edge, federated learning (FL) has gained attention as a promising approach
for training deep learning (DL) models collaboratively across a large number of devices …
for training deep learning (DL) models collaboratively across a large number of devices …
Filling the missing: Exploring generative ai for enhanced federated learning over heterogeneous mobile edge devices
Distributed Artificial Intelligence (AI) model training over mobile edge networks encounters
significant challenges due to the data and resource heterogeneity of edge devices. The …
significant challenges due to the data and resource heterogeneity of edge devices. The …