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 …

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 …

Since its inception in 2016, federated learning has evolved into a highly promising decentral-
ized machine learning approach, facilitating collaborative model training across numerous …

Federated learning is a new learning paradigm that decouples data collection and model
training via multi-party computation and model aggregation. As a flexible learning setting …

Federated learning (FL), a novel distributed machine learning (DML) approach, has been
widely adopted to train deep neural networks (DNNs), over massive data in edge computing …

Federated learning-enabled mobile edge computing implements privacy-preserving
collaborative machine learning of complex models. However, mobile end devices have high …

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 …

Federated Learning (FL) has emerged as a prominent distributed machine learning
framework that enables geographically discrete clients to train a global model …

Federated Averaging (FedAvg) and its variants are prevalent optimization algorithms
adopted in Federated Learning (FL) as they show good model convergence. However, such …

Federated Learning (FL) has become a popular training paradigm in recent years. However,
stragglers are critical bottlenecks in an Internet of Things (IoT) network while training. These …