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Recent advances in deep learning have shown many successful stories in smart healthcare applications with data-driven insight into improving clinical institutions’ quality of care. Excellent deep learning models are heavily data-driven. The more data trained, the more robust and more generalizable the performance of the deep learning model. However, pooling the medical data into centralized storage to train a robust deep learning model faces privacy, ownership, and strict regulation challenges. Federated learning resolves the previous challenges with a shared global deep learning model using a central aggregator server. At the same time, patient data remain with the local party, maintaining data anonymity and security. In this study, first, we provide a comprehensive, up-to-date review of research employing federated learning in healthcare applications. Second, we evaluate a set of recent challenges from a data-centric perspective in federated learning, such as data partitioning characteristics, data distributions, data protection mechanisms, and benchmark datasets. Finally, we point out several potential challenges and future research directions in healthcare applications.
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Data Quality for Security Challenges: Case Studies of Phishing, Malware and Intrusion Detection Datasets
Techniques from data science are increasingly being applied by researchers to security challenges. However, challenges unique to the security domain necessitate painstaking care for the models to be valid and robust. In this paper, we explain key dimensions of data quality relevant for security, illustrate them with several popular datasets for phishing, intrusion detection and malware, indicate operational methods for assuring data quality and seek to inspire the audience to generate high quality datasets for security challenges.
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- Award ID(s):
- 1659755
- PAR ID:
- 10137610
- Date Published:
- Journal Name:
- Conference on Computer and Communications Security
- Page Range / eLocation ID:
- 2605 to 2607
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3319535.3363267
