Distributed learning in wireless networks: Recent progress and future challenges
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 …
applications to efficiently analyze various types of data collected by edge devices for …
From distributed machine learning to federated learning: A survey
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 …
users, various regions or organizations. Because of laws or regulations, the distributed data …
When digital economy meets Web3. 0: Applications and challenges
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 …
amount of attention due to its unique decentralized characteristics. The digital economy is an …
Combining federated learning and edge computing toward ubiquitous intelligence in 6G network: Challenges, recent advances, and future directions
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 …
providing intelligent services in the 6G network calls for Ubiquitous Intelligence (UI). A key to …
The Impact of Adversarial Attacks on Federated Learning: A Survey
Federated learning (FL) has emerged as a powerful machine learning technique that
enables the development of models from decentralized data sources. However, the …
enables the development of models from decentralized data sources. However, the …
From federated learning to federated neural architecture search: a survey
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 …
preservation, which has found a wide range of applications where data privacy is of primary …
Hierarchical domain adaptation projective dictionary pair learning model for EEG classification in IoMT systems
Epilepsy recognition based on electroencephalogram (EEG) and artificial intelligence
technology is the main tool of health analysis and diagnosis in Internet of medical things …
technology is the main tool of health analysis and diagnosis in Internet of medical things …
Communication and computation efficiency in federated learning: A survey
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 …
Intelligence, due to its vital role in preserving data privacy, and eliminating the need to …
Accelerating federated learning with cluster construction and hierarchical aggregation
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 …
and privacy concerns of traditional cloud-based training. However, the existing FL …