… learn these extreme events, assuming they are independently and identically distributed over
VUEs, a novel distributed approach based on federated learning (… of federated learning in …

… and intelligent data analysis at the network edge, federated learning (FL), a distributed
architecture for machine learning, has been introduced to enhance edge intelligence without …

… the use of federated learning in the context of ultra-reliable and low-latency communication.
… work for enabling ultra-reliable and low-latency vehicular communication. We formulate a …

… to integrate blockchain with federated learning to achieve secure and efficient edge
intelligence. In our proposed framework, the federated learning parameters are recorded in the …

… redundancy into federated learning for mitigating stragglers and speeding up the training
procedure. CodedFedL enables coded computing for non-linear federated learning by …

… In light of high-accuracy but high-latency cloud-based FL and low-latency but low-accuracy …
cooperative federated edge learning (CFEL) to enable both high-accuracy and low-latency …

… Federated learning (FL) is a popular method for preserving … is a privacy-preserving and
low-latency FL method that transmits … time when training the learning model with various settings. …

In federated learning (FL), model training is distributed over clients and local models are
aggregated by a central server. The performance of uploaded models in such situations can …

… In this work, we studied the low-latency problem for multitask federated learning in the MEC
networks. Considering in the large-scale IoT scenario, it is not possible for edge nodes and …

… To address the aforementioned issues, researchers have proposed a new distributed
model training framework called Federated Learning (FL) [6], [7]. A typical wireless FL system …