Federated learning (FL) enables multiple clients to collaboratively train machine learning
models without revealing their private training data. In conventional FL, the system follows …

Federated learning (FL) allows multiple clients to collaboratively train a global machine
learning model through a server, without exchanging their private training data. However …

Federated Learning (FL) is a promising distributed learning approach that enables multiple
clients to collaboratively train a shared global model. However, recent studies show that FL …

Federated learning (FL) allows clients at the edge to learn a shared global model without
disclosing their private data. However, FL is susceptible to poisoning attacks, wherein an …

Federated Learning (FL) permits different parties to collaboratively train a global model
without disclosing their respective local labels. A crucial step of FL, that of aggregating local …

Federated Learning (FL) has emerged as a privacy-preserving training paradigm, which
enables distributed devices to jointly learn a shared model without raw data sharing …

Split federated learning (SFL) is a recently proposed distributed collaborative learning
architecture that integrates federated learning (FL) with split learning (SL), offering an …

Federated Learning (FL), a secure and emerging distributed learning paradigm, has
garnered significant interest in the Internet of Things (IoT) domain. However, it remains …

We consider the problem of predicting cellular network performance (signal maps) from
measurements collected by several mobile devices. We formulate the problem within the …

Federated Learning (FL) has evolved into a pivotal paradigm for collaborative machine
learning, enabling a centralised server to compute a global model by aggregating the local …