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Found 6 papers, 1 papers with code
Privacy at a Price: Exploring its Dual Impact on AI Fairness
The worldwide adoption of machine learning (ML) and deep learning models, particularly in critical sectors, such as healthcare and finance, presents substantial challenges in maintaining individual privacy and fairness.
The Frontier of Data Erasure: Machine Unlearning for Large Language Models
Large Language Models (LLMs) are foundational to AI advancements, facilitating applications like predictive text generation.
Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey
Wireless FL (WFL) is a distributed method of training a global deep learning model in which a large number of participants each train a local model on their training datasets and then upload the local model updates to a central server.
Towards Blockchain-Assisted Privacy-Aware Data Sharing For Edge Intelligence: A Smart Healthcare Perspective
Linkage attack is a type of dominant attack in the privacy domain, which can leverage various data sources for private data mining.
Learn to Unlearn: A Survey on Machine Unlearning
This inspired recent research on removing the influence of specific data samples from a trained ML model.
An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation
Asynchronous Federated Learning (AFL) is a scheme that reduces the latency of aggregation to improve efficiency, but the learning performance is unstable due to unreasonably weighted local models.
