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) is a distributed machine learning (ML) approach that enables
models to be trained on client devices while ensuring the privacy of user data. Model …

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 …

With the growing concerns on data security and user privacy, a decentralized mechanism is
implemented for federated data mining (FDM), which can bridge data silos and collaborate …

This paper explores the potential for communication-efficient federated learning (FL) in
modern distributed systems. FL is an emerging distributed machine learning technique that …

The cloud-based solutions are becoming inefficient due to considerably large time delays,
high power consumption, and security and privacy concerns caused by billions of connected …

Federated learning (FL) is typically performed in a synchronous parallel manner, and the
involvement of a slow client delays the training progress. Current FL systems employ a …

Federated learning (FL) protects data privacy through local training and parameter
aggregation. However, there is no need that all users are required to train their local models …

Federated Learning (FL) has gained increasing interest in recent years as a distributed on-
device learning paradigm. However, multiple challenges remain to be addressed for …

In recent years, various machine learning (ML) solutions have been developed to solve
resource management, interference management, autonomy, and decision-making …