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 …
Trusted AI in multiagent systems: An overview of privacy and security for distributed learning
Motivated by the advancing computational capacity of distributed end-user equipment (UE),
as well as the increasing concerns about sharing private data, there has been considerable …
as well as the increasing concerns about sharing private data, there has been considerable …
Decentralized federated learning based on blockchain: concepts, framework, and challenges
Decentralized federated learning integrates advanced technologies, including distributed
computing and secure encryption methodologies, to facilitate a robust and efficient …
computing and secure encryption methodologies, to facilitate a robust and efficient …
Dtqfl: A digital twin-assisted quantum federated learning algorithm for intelligent diagnosis in 5G mobile network
Z Qu, Y Li, B Liu, D Gupta… - IEEE journal of biomedical …, 2023 - ieeexplore.ieee.org
Smart healthcare aims to revolutionize med-ical services by integrating artificial intelligence
(AI). The limitations of classical machine learning include privacy concerns that prevent …
(AI). The limitations of classical machine learning include privacy concerns that prevent …
Semi-federated learning: Convergence analysis and optimization of a hybrid learning framework
Under the organization of the base station (BS), wireless federated learning (FL) enables
collaborative model training among multiple devices. However, the BS is merely responsible …
collaborative model training among multiple devices. However, the BS is merely responsible …
[HTML][HTML] Applications and challenges of federated learning paradigm in the big data era with special emphasis on COVID-19
A Majeed, X Zhang, SO Hwang - Big Data and Cognitive Computing, 2022 - mdpi.com
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 …
guarantees than any other digital solution. Since its inception in 2016, FL has been …
BlockDFL: A Blockchain-based Fully Decentralized Peer-to-Peer Federated Learning Framework
Federated learning (FL) enables the collaborative training of machine learning models
without sharing training data. Traditional FL heavily relies on a trusted centralized server …
without sharing training data. Traditional FL heavily relies on a trusted centralized server …
Blockchain-empowered federated learning through model and feature calibration
With the proliferation of computationally powerful edge devices, edge computing has been
widely adopted for wide-ranging computational tasks. Among these, edge artificial …
widely adopted for wide-ranging computational tasks. Among these, edge artificial …
Blockchain-enabled Trustworthy Federated Unlearning
Federated unlearning is a promising paradigm for protecting the data ownership of
distributed clients. It allows central servers to remove historical data effects within the …
distributed clients. It allows central servers to remove historical data effects within the …
FedComp: A Federated Learning Compression Framework for Resource-Constrained Edge Computing Devices
Top-K sparsification-based compression techniques are popular and powerful for reducing
communication costs in federated learning (FL). However, existing Top-K sparsification …
communication costs in federated learning (FL). However, existing Top-K sparsification …