Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …

Federated learning (FL) is a novel distributed machine learning paradigm that enables
participants to collaboratively train a centralized model with privacy preservation by …

Federated Learning (FL) is a machine learning framework where many clients
collaboratively train models while kee** the training data decentralized. Despite recent …

While federated learning (FL) systems often utilize quantization to battle communication and
computational bottlenecks they have heretofore been limited to deploying fixed-precision …

Abstract The Tactile Internet of Things (TIoT) represents a special class of the Internet of
Things (IoT) that has opened the door for a new generation of agile, highly dynamic …

Federated learning (FL) is a privacy-preserving collective machine learning paradigm.
Vertical federated learning (VFL) deals with the case where participants share the same …

The past years have witnessed the success of a distributed learning system called
Federated Learning (FL). Recently, asynchronous FL (AFL) has demonstrated its potential in …

Motivated by high resource costs of centralized machine learning schemes as well as data
privacy concerns, federated learning (FL) emerged as an efficient alternative that relies on …

In edge computing (EC), federated learning (FL) enables numerous distributed devices (or
workers) to collaboratively train AI models without exposing their local data. Most works of …

Quantized neural networks (QNNs) have received increasing attention in resource-
constrained scenarios due to their exceptional generalizability. However, their robustness …