In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …

The next-generation of wireless networks will enable many machine learning (ML) tools and
applications to efficiently analyze various types of data collected by edge devices for …

Federated Learning (FL) is a decentralized machine-learning paradigm, in which a global
server iteratively averages the model parameters of local users without accessing their data …

In recent years, deep neural networks have been successful in both industry and academia,
especially for computer vision tasks. The great success of deep learning is mainly due to its …

Federated learning is a privacy-preserving machine learning technique to train intelligent
models from decentralized data, which enables exploiting private data by communicating …

This article proposes an efficient federated distillation learning system (EFDLS) for multitask
time series classification (TSC). EFDLS consists of a central server and multiple mobile …

With more regulations tackling the protection of users' privacy-sensitive data in recent years,
access to such data has become increasingly restricted. A new decentralized training …

Machine learning (ML) is a promising enabler for the fifth-generation (5G) communication
systems and beyond. By imbuing intelligence into the network edge, edge nodes can …

Federated distillation (FD) is a popular novel algorithmic paradigm for Federated learning
(FL), which achieves training performance competitive to prior parameter averaging-based …

FedLab is a lightweight open-source framework for the simulation of federated learning. The
design of FedLab focuses on federated learning algorithm effectiveness and communication …