Edge intelligence refers to utilize a large number of distributed data and computing
resources to learn and inference directly at network edge. Federated edge learning (FEEL) …

Edge machine learning involves the development of learning algorithms at the network edge
to leverage massive distributed data and computation resources. Among others, the …

Federated learning is a model-level aggregation learning paradigm, which can generate
high quality models without collecting the local private data of users. As a distributed …

Federated edge learning (FEEL) is a promising distributed learning technique for next-
generation wireless networks. FEEL preserves the user's privacy, reduces the …

Federated learning (FL) and edge computing are both important technologies to support the
future Internet of Things (IoT). Despite that the network supported by edge computing has …

Deploying federated learning at the wireless edge introduces federated edge learning
(FEEL). Given FEEL's limited communication resources and potential mislabeled data on …

Clustered Federated Multitask Learning (CFL) was introduced as an efficient scheme to
obtain reliable specialized models when data is imbalanced and distributed in a non-iid …

To exploit the vast amount of distributed data across edge devices, Federated Learning (FL)
has been proposed to learn a shared model by performing distributed training locally on …

Federated Learning (FL) is an emerging technique to enhance edge intelligence, where
mobile devices train machine learning models collaboratively with their local data. Limited …

To achieve an efficient federated edge learning (FEEL) system, the scheme of device
scheduling and resource allocation should jointly perceive the device availability, wireless …