5 papers
One Round Is All You Need: Analytic Federated Learning for Task-Heterogeneous Multi-Label Medical Image Classification
Afsaneh Mahanipour, Hana Khamfroush
Federated learning (FL) enables multiple clinical institutions to collaboratively train a shared disease classifier without centralizing patient data. In practice, however, each in…
Semi-Supervised Federated Multi-Label Feature Selection with Fuzzy Information Measures
Afsaneh Mahanipour, Hana Khamfroush
Multi-label feature selection (FS) reduces the dimensionality of multi-label data by removing irrelevant, noisy, and redundant features, thereby boosting the performance of multi-l…
Embedded Federated Feature Selection with Dynamic Sparse Training: Balancing Accuracy-Cost Tradeoffs
Afsaneh Mahanipour, Hana Khamfroush
Federated Learning (FL) enables multiple resource-constrained edge devices with varying levels of heterogeneity to collaboratively train a global model. However, devices with limit…
FMLFS: A Federated Multi-Label Feature Selection Based on Information Theory in IoT Environment
Afsaneh Mahanipour, Hana Khamfroush
In certain emerging applications such as health monitoring wearable and traffic monitoring systems, Internet-of-Things (IoT) devices generate or collect a huge amount of multi-labe…
Enhancing IoT Security: A Novel Feature Engineering Approach for ML-Based Intrusion Detection Systems
Afsaneh Mahanipour, Hana Khamfroush
The integration of Internet of Things (IoT) applications in our daily lives has led to a surge in data traffic, posing significant security challenges. IoT applications using cloud…