In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …

Full leverage of the huge volume of data generated on a large number of user devices for
providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to …

The proliferation in embedded and communication technologies made the concept of the
Internet of Medical Things (IoMT) a reality. Individuals' physical and physiological status can …

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 …

Federated learning (FL) is a machine learning technique that enables distributed devices to
train a learning model collaboratively without sharing their local data. FL-based systems can …

Distributed learning is envisioned as the bedrock of next-generation intelligent networks,
where intelligent agents, such as mobile devices, robots, and sensors, exchange information …

In this paper, we study a new latency optimization problem for blockchain-based federated
learning (BFL) in multi-server edge computing. In this system model, distributed mobile …

The ultra-low latency requirements of 5G/6G applications and privacy constraints call for
distributed machine learning systems to be deployed at the edge. With its simple yet …

Federated learning (FL) has emerged as a distributed machine learning (ML) technique to
train models without sharing users' private data. In this paper, we introduce a decentralized …

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 …