Edge Computing (EC) is a new architecture that extends Cloud Computing (CC) services
closer to data sources. EC combined with Deep Learning (DL) is a promising technology …

Federated learning (FL) is a kind of distributed machine learning framework, where the
global model is generated on the centralized aggregation server based on the parameters of …

Federated learning (FL) is a secure distributed machine learning paradigm that addresses
the issue of data silos in building a joint model. Its unique distributed training mode and the …

Federated machine unlearning (FMU) aims to remove the influence of a specified subset of
training data upon request from a trained federated learning model. Despite achieving …

The problem of Byzantine resilience in distributed machine learning, aka Byzantine machine
learning, consists of designing distributed algorithms that can train an accurate model …

The federated learning (FL) framework enables edge clients to collaboratively learn a
shared inference model while keeping privacy of training data on clients. Recently, many …

In federated learning (FL), a cluster of local clients are chaired under the coordination of the
global server and cooperatively train one model with privacy protection. Due to the multiple …

Federated Learning (FL) is a platform for smart healthcare systems that use wearables and
other Internet of Things enabled devices. However, source inference attacks (SIAs) can infer …

As a result of the advancement in the fourth industrial revolution and communication
technology, the use of digital twins (DT) and federated learning (FL) in the industrial Internet …

In a federated learning scenario where multiple parties jointly learn a model from their
respective data, there exist two conflicting goals for the choice of appropriate algorithms. On …