A novel Decentralized Noisy Model Update Tracking Federated Learning algorithm
(FedNMUT) is proposed, which is tailored to function efficiently in the presence of noisy …

We present a semi-decentralized federated learning algorithm wherein clients collaborate
by relaying their neighbors' local updates to a central parameter server (PS). At every …

Intermittent connectivity of clients to the parameter server (PS) is a major bottleneck in
federated edge learning frameworks. The lack of constant connectivity induces a large …

Federated learning and gossip learning are emerging methodologies designed to mitigate
data privacy concerns by retaining training data on client devices and exclusively sharing …

The effectiveness of federated learning (FL) in leveraging distributed datasets is highly
contingent upon the accuracy of model exchanges between clients and servers …

As distributed learning applications such as Federated Learning, the Internet of Things (IoT),
and Edge Computing grow, it is critical to address the shortcomings of such technologies …

Communication errors caused by noisy links can negatively impact the accuracy of
federated learning (FL) algorithms. To address this issue, we introduce an FL algorithm that …

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 …

Intermittent connectivity of clients to the parameter server (PS) is a major bottleneck in
federated edge learning frameworks. The lack of constant connectivity induces a large …

Federated learning is a popular distributed learning approach for training a machine
learning model without disclosing raw data. It consists of a parameter server and a possibly …