Abstract Machine Learning (ML) algorithms are used to train the machines to perform
various complicated tasks that begin to modify and improve with experiences. It has become …

Federated learning (FL) has emerged as a powerful machine learning technique that
enables the development of models from decentralized data sources. However, the …

Federated Learning (FL) facilitates clients to collaborate on training a shared machine
learning model without exposing individual private data. Nonetheless FL remains …

Federated Learning (FL) is a distributed Deep Learning (DL) technique that creates a global
model through the local training of multiple edge devices. It uses a central server for model …

The emerging integration of Internet of Things (IoT) and AI has unlocked numerous
opportunities for innovation across diverse industries. However, growing privacy concerns …

Heterogeneity across devices in federated learning (FL) typically refers to statistical (eg, non-
iid data distributions) and resource (eg, communication bandwidth) dimensions. In this …

As federated learning (FL) becomes more widespread, there is growing interest in its
decentralized variants. Decentralized FL leverages the benefits of fast and energy-efficient …

Federated Learning (FL) stands as a robust framework facilitating collaborative learning in
distributed Internet of Things (IoT) settings. Nevertheless, the proliferation of non-identically …

There has been recent interest in leveraging federated learning (FL) for radio signal
classification tasks. In FL, model parameters are periodically communicated from …

In this paper, we propose a robust federated meta-learning framework for training end-to-
end communication systems in a cell-free scenario to address the challenges posed by …