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 …

In recent years, data and computing resources are typically distributed in the devices of end
users, various regions or organizations. Because of laws or regulations, the distributed data …

Federated learning is an emerging distributed machine learning framework for privacy
preservation. However, models trained in federated learning usually have worse …

With the continuous development of web technology, Web3. 0 has attracted a considerable
amount of attention due to its unique decentralized characteristics. The digital economy is an …

Full leverage of the huge volume of data generated on a large number of user devices for
providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to …

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 is a recently proposed distributed machine learning paradigm for privacy
preservation, which has found a wide range of applications where data privacy is of primary …

Epilepsy recognition based on electroencephalogram (EEG) and artificial intelligence
technology is the main tool of health analysis and diagnosis in Internet of medical things …

Federated Learning is a much-needed technology in this golden era of big data and Artificial
Intelligence, due to its vital role in preserving data privacy, and eliminating the need to …

Federated learning (FL) has emerged in edge computing to address the limited bandwidth
and privacy concerns of traditional cloud-based training. However, the existing FL …