Machine Learning (ML) algorithms process input data making it possible to recognize and
extract patterns from a large data volume. Likewise, Internet of Things (IoT) devices provide …

Federated learning (FL) is an efficient decentralized machine learning methodology for
processing nonindependent and identically distributed (non-IID) data due to geographical …

Federated learning (FL) has received considerable attention because it allows multiple
devices to train models locally without revealing sensitive data. Well-trained local models …

Consumer electronic devices often involve processing and analyzing a large amount of user
personal data. Nevertheless, owing to apprehensions regarding privacy and security, users …

Federated learning (FL), as a machine learning framework, has garnered substantial
attention from researchers in recent years. FL makes it possible to train a global model …

Knowledge graphs (KGs) are emerging data models allowing data providers to share data.
This data sharing might bring new knowledge and collaborations, with evident benefits for …

Federated learning (FL) allows the collaborative training of a collective model by a vast
number of decentralized clients while ensuring that these clients' data remain private and …

Abstract In Cyber-Physical Systems (CPS), distributed learning is essential for efficiently
handling complex tasks when sufficient resources are available. However, when resources …

In the realm of targeted advertising, the demand for precision is paramount, and the
traditional centralized machine learning paradigm fails to address this necessity effectively …

Federal learning based on parameter sharing under the assumption that the data obey
independent identical distribution (IID has already achieved good results in areas such as …