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 …

Federated learning (FL) has emerged as a promising paradigm for collaborative model
training while preserving data privacy. Client selection plays a crucial role in determining the …

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 …

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

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

Due to the contradiction between limited bandwidth and huge transmission parameters,
federated Learning (FL) has been an ongoing challenge to reduce the model parameters …

Radio Access Networks (RAN) are anticipated to gradually transition towards Cloud RAN (C-
RAN), leveraging the full advantages of the cloud-native computing model. While this …