There is significant interest in edge computing (EC) for computational social systems to
process and store data at the edge of the network. One of the key applications of EC is to …

With the exponential growth of data created at the network edge, decentralized and Gossip-
based training of deep learning (DL) models on edge computing (EC) gains tremendous …

In edge computing (EC), federated learning (FL) enables massive devices to collaboratively
train AI models without exposing local data. In order to avoid the possible bottleneck of the …

Today's state-of-the-art machine learning (ML) techniques, such as deep learning (DL)
networks are typically trained using cloud platforms, leveraging elastic scalability of the …

Decentralized federated learning (DFL) is a promising technique to enable distributed
machine learning over edge nodes without relying on a centralized parameter server …

Federated learning (FL) has been widely adopted to train machine learning models over
massive distributed data sources in edge computing. However, the existing FL frameworks …

Recently, federated learning (FL) techniques have enabled multiple users to train machine
learning models collaboratively without data sharing. However, existing FL algorithms suffer …

Federated learning (FL) enables collaborative model training over distributed computing
nodes without sharing their privacy-sensitive raw data. However, in FL, iterative exchanges …

Federated learning (FL) is a technique for distributed machine learning (ML), in which edge
devices carry out local model training on their individual datasets. In traditional FL …

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 …