In recent years, data and computing resources are typically distributed in the devices of end
users, various regions or organizations. Because of laws or regulations, the distributed data …

The success of machine learning (ML) techniques in the formerly difficult areas of data
analysis and pattern extraction has led to their widespread incorporation into various …

In the traditional distributed machine learning scenario, the user's private data is transmitted
between clients and a central server, which results in significant potential privacy risks. In …

Data mining is a process to extract unknown, hidden, and potentially useful information from
data. But the problem of data island makes it arduous for people to collect and analyze …

Federated Learning is a much-needed technology in this golden era of big data and Artificial
Intelligence, due to its vital role in preserving data privacy, and eliminating the need to …

Federated Learning (FL) is a powerful technique to train a model on a server with data from
several clients in a privacy-preserving manner. FL incurs significant communication costs …

Coronavirus (COVID-19) has caused a global disaster with adverse effects on global health
and the economy. Early detection of COVID-19 symptoms will help to reduce the severity of …

Federated learning (FL) is a distributed model for deep learning that integrates client‐server
architecture, edge computing, and real‐time intelligence. FL has the capability of …

Federated learning (FL) is a distributed machine learning paradigm that ensures data do not
leave local devices. Data sharing problems can be addressed by FL in untrusted …

Federated learning (FL) is a machine learning approach that decentralizes data and its
processing by allowing clients to train intermediate models on their devices with locally …