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 …

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 is a machine learning technique that permits clients to train the model at
a local site in a collaborative manner. It builds a global shared model on the basis of …

Recently, in response to the low efficiency and high transmission latency of traditional
centralized content delivery networks, especially in congested scenarios, edge caching has …

Federated learning (FL) can suffer from communication bottlenecks when deployed in
mobile networks, limiting participating clients and deterring FL convergence. In this context …

Federated learning (FL) is a viable technique to train a shared machine learning model
without sharing data. Hierarchical FL (HFL) system has yet to be studied regrading its …

Recently, mobile and embedded devices worldwide generate a massive amount of data at
the network edge. To efficiently exploit the data from distributed devices, we concentrate on …

Federated learning (FL) is a distributed machine learning framework that enables the
training of shared models without the need to share local data. However, FL faces …

Federated Learning (FL) has emerged as a transformative artificial intelligence paradigm,
facilitating knowledge sharing among distributed edge devices while upholding data …

To protect user privacy and data security, the integration of Federated Learning (FL) and
blockchain has become an emerging research hotspot. However, the limited throughput and …