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Found 7 papers, 1 papers with code
Uncovering Promises and Challenges of Federated Learning to Detect Cardiovascular Diseases: A Scoping Literature Review
Overall, this survey paper aims to provide a comprehensive overview of the current state-of-the-art in FL for CVD detection and to highlight its potential for improving the accuracy and privacy of CVD detection models.
Federated-Learning-Based Anomaly Detection for IoT Security Attacks
This is to say that classic ML works on the legacy set of entire data located on a central server, which makes it the least preferred option for domains with privacy concerns on user data.
Fairness in Federated Learning for Spatial-Temporal Applications
Federated learning involves training statistical models over remote devices such as mobile phones while keeping data localized.
Communication Efficiency in Federated Learning: Achievements and Challenges
Over the years, this has become an emerging technology especially with various data protection and privacy policies being imposed FL allows performing machine learning tasks whilst adhering to these challenges.
A Survey of Machine Learning Techniques in Adversarial Image Forensics
Image forensic plays a crucial role in both criminal investigations (e. g., dissemination of fake images to spread racial hate or false narratives about specific ethnicity groups) and civil litigation (e. g., defamation).
A Federated Approach for Fine-Grained Classification of Fashion Apparel
As online retail services proliferate and are pervasive in modern lives, applications for classifying fashion apparel features from image data are becoming more indispensable.
An Ensemble Deep Learning-based Cyber-Attack Detection in Industrial Control System
The integration of communication networks and the Internet of Things (IoT) in Industrial Control Systems (ICSs) increases their vulnerability towards cyber-attacks, causing devastating outcomes.
