Federated learning (FL) is a kind of distributed machine learning framework, where the
global model is generated on the centralized aggregation server based on the parameters of …

Federated learning (FL) has experienced a boom in recent years, which is jointly promoted
by the prosperity of machine learning and Artificial Intelligence along with emerging privacy …

Recently, innovations in the Internet of Medical Things (IoMT), information and
communication technologies, and machine learning (ML) have enabled smart healthcare …

Since the traffic conditions change over time, machine learning models that predict traffic
flows must be updated continuously and efficiently in smart public transportation. Federated …

To build intelligent model learning in conventional architecture, the local data are required to
be transmitted toward the cloud server, which causes heavy backhaul congestion, leakage …

Federated learning (FL) enables learning a model from data distributed across numerous
workers while preserving data privacy. However, the classical FL technique is designed for …

Blockchain, as the foundation for cryptocurrencies, has recently garnered significant
attention. It serves as an immutable distributed ledger technology, which allows transactions …

Federated learning (FL) is a distributed framework for machine learning that enables
collaborative training of a shared model across data silos while preserving data privacy …

Federated learning (FL) enables collaborative training of a shared model on edge devices
while maintaining data privacy. FL is effective when dealing with independent and …

Federated learning (FL) is a distributed machine learning approach that protects user data
privacy by training models locally on clients and aggregating them on a parameter server …