The current state and challenges of fairness in federated learning
S Vucinich, Q Zhu - IEEE Access, 2023 - ieeexplore.ieee.org
The proliferation of artificial intelligence systems and their reliance on massive datasets
have led to a renewed demand on privacy of data. Both the large data processing need and …
have led to a renewed demand on privacy of data. Both the large data processing need and …
Trustworthy Federated Learning: A Comprehensive Review, Architecture, Key Challenges, and Future Research Prospects
Federated Learning (FL) emerged as a significant advancement in the field of Artificial
Intelligence (AI), enabling collaborative model training across distributed devices while …
Intelligence (AI), enabling collaborative model training across distributed devices while …
Fed-PEMC: A privacy-enhanced federated deep learning algorithm for consumer electronics in mobile edge computing
Q Lin, S Jiang, Z Zhen, T Chen… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Consumer electronic devices often involve processing and analyzing a large amount of user
personal data. Nevertheless, owing to apprehensions regarding privacy and security, users …
personal data. Nevertheless, owing to apprehensions regarding privacy and security, users …
Fed-MPS: Federated learning with local differential privacy using model parameter selection for resource-constrained CPS
Abstract In Cyber-Physical Systems (CPS), distributed learning is essential for efficiently
handling complex tasks when sufficient resources are available. However, when resources …
handling complex tasks when sufficient resources are available. However, when resources …
Differential Privacy-Aware Generative Adversarial Network-Assisted Resource Scheduling for Green Multi-Mode Power IoT
S Zhang, J Xue, J Liu, Z Zhou, X Chen… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
The low-carbon and efficient operation of smart parks requires high-precision and real-time
energy management model training. Multi-mode power Internet of Things (PIoT) consisting …
energy management model training. Multi-mode power Internet of Things (PIoT) consisting …
On the Differential Privacy in Federated Learning based on Over-the-Air Computation
The federated learning is a promising machine learning technique to bring about advanced
services and application for future industries. It has been known that the federated learning …
services and application for future industries. It has been known that the federated learning …
[HTML][HTML] A linkable signature scheme supporting batch verification for privacy protection in crowd-sensing
X Li, G Jeon, W Wang, J Zhao - Digital Communications and Networks, 2024 - Elsevier
The maturity of 5G technology has enabled crowd-sensing services to collect multimedia
data over wireless network, so it has promoted the applications of crowd-sensing services in …
data over wireless network, so it has promoted the applications of crowd-sensing services in …
An Efficient Asynchronous Federated Learning Protocol for Edge Devices
Q Li, Z Gao, Y Sun, Y Wang, R Wang… - IEEE Internet of Things …, 2024 - ieeexplore.ieee.org
Recent studies highlight the significant potential of edge computing and federated learning
in advancing artificial intelligence. However, challenges such as unstable device …
in advancing artificial intelligence. However, challenges such as unstable device …