Federated learning (FL) is one of the leading paradigms of modern times with higher privacy
guarantees than any other digital solution. Since its inception in 2016, FL has been …

The federated learning (FL) approach in machine learning preserves user privacy during
data collection. However, traditional FL schemes still rely on a centralized server, making …

Owing to the universal availability of 5G networking networks, both business and academia
have started to explore 6G interchanges. 6G is widely intended to be based on artificial …

Today, the rapid growth of the internet and advancements in mobile technology and
increased internet connectivity have brought us to a data-driven economy where an …

In recent years, more and more attention has been paid to the privacy issues associated with
storing user data in a centralized manner. In fact, strict laws have been introduced to …

Federated learning (FL) provides convenience for cross-domain machine learning
applications and has been widely studied. However, the original FL is still vulnerable to …

One of the new trends in the field of artificial intelligence is federated learning (FL), which
will have promising roles in many real-world applications due to the work characteristics of …

Federated learning (FL) has emerged as one of the de-facto privacy-preserving paradigms
that can effectively work with decentralized data sources (eg, hospitals) without acquiring …

Federated learning (FL) has received a great deal of research attention in the context of
privacy protection restrictions. By jointly training deep learning models, a variety of training …

The machine learning paradigms driven by the sixth-generation network (6G) facilitate an
ultra-fast and low-latency communication environment. However, specific research and …