The increasingly deeper neural networks hinder the democratization of privacy-enhancing
distributed learning, such as federated learning (FL), to resource-constrained devices. To …

With the arising concerns of privacy within machine learning, federated learning (FL) was
invented in 2017, in which the clients, such as mobile devices, train a model and send the …

Decentralized federated learning integrates advanced technologies, including distributed
computing and secure encryption methodologies, to facilitate a robust and efficient …

Federated learning (FL) has emerged as a promising paradigm that enables clients to
collaboratively train a shared global model without uploading their local data. To alleviate …

Pervasive new era applications are expected to involve massive amount of data to
implement intelligent distributed frameworks based on machine learning, supported by sixth …

Federated learning (FL) enables collaborative model training over distributed computing
nodes without sharing their privacy-sensitive raw data. However, in FL, iterative exchanges …

To fully exploit enormous data generated by intelligent devices in edge computing, edge
federated learning (EFL) is envisioned as a promising solution. The distributed collaborative …

In cross-silo federated learning (FL), organizations cooperatively train a global model with
their local datasets. However, some organizations may act as free riders such that they only …

Federated Learning (FL) has become a popular decentralized learning paradigm to train a
machine learning model using distributed mobile devices without compromising user …

Federated learning (FL) is a new paradigm for privacy-preserving learning. This is
particularly appealing in the mobile edge network (MEN), in which devices collectively train …