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Privacy preservation in Artificial Intelligence and Extended Reality (AI-XR) metaverses: A survey
The metaverse is a nascent concept that envisions a virtual universe, a collaborative space
where individuals can interact, create, and participate in a wide range of activities. Privacy in …
where individuals can interact, create, and participate in a wide range of activities. Privacy in …
A Profit-Maximizing Data Marketplace with Differentially Private Federated Learning under Price Competition
The proliferation of machine learning (ML) applications has given rise to a new and popular
data marketplace paradigm. These marketplaces facilitate ML model requesters in obtaining …
data marketplace paradigm. These marketplaces facilitate ML model requesters in obtaining …
Collaboration in federated learning with differential privacy: A stackelberg game analysis
As a privacy-preserving distributed learning paradigm, federated learning (FL) enables
multiple client devices to train a shared model without uploading their local data. To further …
multiple client devices to train a shared model without uploading their local data. To further …
Incentive mechanism design for Federated Learning with Stackelberg game perspective in the industrial scenario
W Guo, Y Wang, P Jiang - Computers & Industrial Engineering, 2023 - Elsevier
Federated Learning (FL) is a typical decentralized Machine Learning framework in which
clients invest resources to train their local models without sharing their data and then …
clients invest resources to train their local models without sharing their data and then …
[HTML][HTML] Distributed cooperative automatic modulation classification using dwa-admm in wireless communication networks
Automatic modulation classification (AMC) is an important component in non-cooperative
wireless communication networks to identify the modulation schemes of the received …
wireless communication networks to identify the modulation schemes of the received …
Communication-efficient federated learning over capacity-limited wireless networks
In this paper, we propose a communication-efficient federated learning (FL) framework to
enhance the convergence rate of FL under limited uplink capacity. The core idea of our …
enhance the convergence rate of FL under limited uplink capacity. The core idea of our …
Distributed deep learning-based signal classification for time–frequency synchronization in wireless networks
In this paper, we propose a novel distributed deep learning (DL) network for signal
classification to achieve accurate time–frequency synchronization in wireless …
classification to achieve accurate time–frequency synchronization in wireless …
Practical Implementation of Federated Learning for Detecting Backdoor Attacks in a Next-word Prediction Model
JKW Wong, KK Chung, YW Lo, CY Lai, SWY Mung - Scientific Reports, 2025 - nature.com
This article details the development of a next-word prediction model utilizing federated
learning and introduces a mechanism for detecting backdoor attacks. Federated learning …
learning and introduces a mechanism for detecting backdoor attacks. Federated learning …
ADPF: Anti-inference differentially private protocol for federated learning
Z Zhao, Z Lin, Y Sun - Computer Networks, 2025 - Elsevier
With the popularity of commercial artificial intelligence (AI), the importance of individual data
is constantly increasing for the construction of large models. To ensure the utility of the …
is constantly increasing for the construction of large models. To ensure the utility of the …
EntroCFL: Entropy-Based Clustered Federated Learning With Incentive Mechanism
Federated learning (FL) emerged as a machine learning approach in situations where the
privacy of sensitive data needs to be protected. Within the FL framework, clients collaborate …
privacy of sensitive data needs to be protected. Within the FL framework, clients collaborate …