Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges
In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …
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 …
Lead federated neuromorphic learning for wireless edge artificial intelligence
In order to realize the full potential of wireless edge artificial intelligence (AI), very large and
diverse datasets will often be required for energy-demanding model training on resource …
diverse datasets will often be required for energy-demanding model training on resource …
Client selection in federated learning: Principles, challenges, and opportunities
As a privacy-preserving paradigm for training machine learning (ML) models, federated
learning (FL) has received tremendous attention from both industry and academia. In a …
learning (FL) has received tremendous attention from both industry and academia. In a …
Decentralized federated learning: A survey and perspective
Federated learning (FL) has been gaining attention for its ability to share knowledge while
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …
maintaining user data, protecting privacy, increasing learning efficiency, and reducing …
BFLS: Blockchain and Federated Learning for sharing threat detection models as Cyber Threat Intelligence
T Jiang, G Shen, C Guo, Y Cui, B **e - Computer Networks, 2023 - Elsevier
Abstract Recently, Cyber Threat Intelligence (CTI) sharing has become an important weapon
for cyber defenders to mitigate the increasing number of cyber attacks in a proactive and …
for cyber defenders to mitigate the increasing number of cyber attacks in a proactive and …
Byzantine-resilient decentralized stochastic optimization with robust aggregation rules
This article focuses on decentralized stochastic optimization in the presence of Byzantine
attacks. During the optimization process, an unknown number of malfunctioning or malicious …
attacks. During the optimization process, an unknown number of malfunctioning or malicious …
Challenges and remedies to privacy and security in aigc: Exploring the potential of privacy computing, blockchain, and beyond
Artificial Intelligence Generated Content (AIGC) is one of the latest achievements in AI
development. The content generated by related applications, such as text, images and …
development. The content generated by related applications, such as text, images and …
Privacy computing meets metaverse: Necessity, taxonomy and challenges
Metaverse, the core of the next-generation Internet, is a computer-generated holographic
digital environment that simultaneously combines spatio-temporal, immersive, real-time …
digital environment that simultaneously combines spatio-temporal, immersive, real-time …
Advancements in federated learning: Models, methods, and privacy
Federated learning (FL) is a promising technique for resolving the rising privacy and security
concerns. Its main ingredient is to cooperatively learn the model among the distributed …
concerns. Its main ingredient is to cooperatively learn the model among the distributed …