… , multiaccess edge computing (MEC) and federated learning (FL) … edge of the industrial
systems, while FL leverages the edge resources to enable a privacyaware collaborative learning, …

… gradient sparsification in federated learning. Every iteration, … the wireless bandwidth needed
in federated learning. However, … (GGS) framework for federated learning in edge computing …

… user-owned devices in edge computing with distributed and … of the volatile edge computing
architectures and topologies in … FL and edge computing networks, we discuss various edge …

… We also verify the Poisson property of the task arrival for each server pool in the edge server.
… the Federated Learning based method to estimate the execution time in the edge server. …

… Motivated by this, we propose an innovative method, federated learning with asynchronous
convergence (FedAC) considering a staleness coefficient, while using a blockchain network …

… IoT data to be gathered on edge servers and gateways, where federated learning (FL) can be
… of edge computing and federated learning, a decentralized machine learning methodology …

… In this article, since federated learning can protect the data privacy of end users, we introduce
it into edge computing for training a unified deep learning model. With federated learning, …

… heterogeneity in mobile edge computing environment. … edge computing environment (eg,
asynchronous federated … efficiency in heterogeneous mobile edge computing environment, we …

… 2.1 Federated Learning FL solves the training tasks with a loose federation of participating
devices (eg, laptops, … His research interests include edge computing and federated learning. …

In Federated Learning (FL), a global statistical model is developed by encouraging mobile
users to perform the model training on their local data and aggregating the output local model …