Semantics-empowered communications: A tutorial-cum-survey
Along with the springing up of the semantics-empowered communication (SemCom)
research, it is now witnessing an unprecedentedly growing interest towards a wide range of …
research, it is now witnessing an unprecedentedly growing interest towards a wide range of …
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 …
When AI meets information privacy: The adversarial role of AI in data sharing scenario
A Majeed, SO Hwang - IEEE Access, 2023 - ieeexplore.ieee.org
Artificial intelligence (AI) is a transformative technology with a substantial number of practical
applications in commercial sectors such as healthcare, finance, aviation, and smart cities. AI …
applications in commercial sectors such as healthcare, finance, aviation, and smart cities. AI …
Holistic survey of privacy and fairness in machine learning
Privacy and fairness are two crucial pillars of responsible Artificial Intelligence (AI) and
trustworthy Machine Learning (ML). Each objective has been independently studied in the …
trustworthy Machine Learning (ML). Each objective has been independently studied in the …
Differential privacy, linguistic fairness, and training data influence: Impossibility and possibility theorems for multilingual language models
Abstract Language models such as mBERT, XLM-R, and BLOOM aim to achieve
multilingual generalization or compression to facilitate transfer to a large number of …
multilingual generalization or compression to facilitate transfer to a large number of …
Quantifying the vulnerability of attributes for effective privacy preservation using machine learning
A Majeed, SO Hwang - IEEE Access, 2023 - ieeexplore.ieee.org
Personal data have been increasingly used in data-driven applications to improve quality of
life. However, privacy preservation of personal data while sharing it with …
life. However, privacy preservation of personal data while sharing it with …
Sok: Unintended interactions among machine learning defenses and risks
Machine learning (ML) models cannot neglect risks to security, privacy, and fairness.
Several defenses have been proposed to mitigate such risks. When a defense is effective in …
Several defenses have been proposed to mitigate such risks. When a defense is effective in …
Security and Privacy in Machine Learning for Health Systems: Strategies and Challenges
EJ de Aguiar, C Traina Jr… - Yearbook of Medical …, 2023 - thieme-connect.com
Objectives: Machine learning (ML) is a powerful asset to support physicians in decision-
making procedures, providing timely answers. However, ML for health systems can suffer …
making procedures, providing timely answers. However, ML for health systems can suffer …
An introduction to adversarially robust deep learning
The widespread success of deep learning in solving machine learning problems has fueled
its adoption in many fields, from speech recognition to drug discovery and medical imaging …
its adoption in many fields, from speech recognition to drug discovery and medical imaging …
Incremental federated learning for traffic flow classification in heterogeneous data scenarios
This paper explores the comparative analysis of federated learning (FL) and centralized
learning (CL) models in the context of multi-class traffic flow classification for network …
learning (CL) models in the context of multi-class traffic flow classification for network …