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 …

The increasingly deeper neural networks hinder the democratization of privacy-enhancing
distributed learning, such as federated learning (FL), to resource-constrained devices. To …

Federated Learning (FL) trains a machine learning model on distributed clients without
exposing individual data. Unlike centralized training that is usually based on carefully …

This paper considers the problem of distributed learning (DL) in the presence of stragglers.
For this problem, DL methods based on gradient coding have been widely investigated …

Cloud and edge computing have proposed different functionalities to enable multiple
applications requiring different communication, computing, and caching (3C) resources. The …

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) is a promising privacy-preserving distributed learning framework
where a server aggregates models updated by multiple devices without accessing their …

Federated learning (FL) is widely regarded as highly promising because it enables the
collaborative training of high-performance machine learning models among a large number …

More devices are being introduced to the cellular network every day, and it is time to think
about how the next generation will deal with them. The 6G will bring new ways to think about …

Federated Learning (FL) has attracted many attentions because of its ability to ensure data
privacy and security. In FL, due to the contradiction between limited bandwidth and huge …