With the rapid growth of the number of devices generating and collecting data, dispersion
becomes an important feature of data in Internet of Things. Federated learning (FL) provides …

Federated learning (FL) training global machine learning models over distributed edge
devices has attracted sustained attentions. However, the heterogeneity of client data …

Distributed stochastic gradient descent (SGD) algorithms are becoming popular in speeding
up deep learning model training by employing multiple computational devices (named …

Federated learning (FL) is an emerging paradigm to train model with distributed data from
numerous Internet of Things (IoT) devices. It inherently assumes a uniform capacity among …

The booming growth of the internet of things has brought about widespread deployment of
devices and massive amounts of sensing data to be processed. Federated learning (FL) …

Federated learning enables multiple clients to collaboratively train a shared model without
transmitting their data. Although this novel approach offers significant advantages in data …

In the realm of decentralized optimization, addressing communication bottlenecks has
emerged as a crucial strategy. While previous research has explored local updates, few …

Financial institutions may be subject to financial fraud by malicious users because of the
large amount of transaction data and sensitive user information involved. Therefore, it is …