Federated learning (FL) has gained attention due to the exponential growth of data.
However, the Non-IID nature of local data introduces bias into the global model during …

Personalized federated learning (PFL) is a subfield of federated learning. Contrary to
conventional federated learning that expects to find a general global model, PFL generates …

Client and Internet of Things devices are increasingly equipped with the ability to sense,
process, and communicate data with high efficiency. This is resulting in a major shift in …

As an emerging technology, federated learning (FL) involves training machine learning
models over distributed edge devices, which attracts sustained attention and has been …

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 …

In federated learning, the non-IID data generated from heterogeneous clients may reduce
the global model efficiency. Previous studies use personalization as a common approach to …

Federated learning is a model-level aggregation learning paradigm, which can generate
high quality models without collecting the local private data of users. As a distributed …

Recent years have witnessed a huge demand for artificial intelligence and machine learning
applications in wireless edge networks to assist individuals with real-time services …

Federated Learning (FL) is a promising machine learning paradigm to cooperatively train a
global model with highly distributed data located on mobile devices. Aiming to optimize the …

Federated learning is currently a popular distributed machine learning solution that often
experiences cumbersome communication processes and challenging model convergence …