110 citations · 126 across the 9 of their papers we have counts for
9 papers
Improved Localized Machine Unlearning Through the Lens of Memorization
Reihaneh Torkzadehmahani, Reza Nasirigerdeh, Georgios Kaissis +3
Machine unlearning refers to removing the influence of a specified subset of training data from a machine learning model, efficiently, after it has already been trained. This is im…
Label Noise-Robust Learning using a Confidence-Based Sieving Strategy
Reihaneh Torkzadehmahani, Reza Nasirigerdeh, Daniel Rueckert +1
In learning tasks with label noise, improving model robustness against overfitting is a pivotal challenge because the model eventually memorizes labels, including the noisy ones. I…
Kernel Normalized Convolutional Networks
Reza Nasirigerdeh, Reihaneh Torkzadehmahani, Daniel Rueckert +1
Existing convolutional neural network architectures frequently rely upon batch normalization (BatchNorm) to effectively train the model. BatchNorm, however, performs poorly with sm…
The FeatureCloud AI Store for Federated Learning in Biomedicine and Beyond
Julian Matschinske, Julian Späth, Reza Nasirigerdeh +29
Machine Learning (ML) and Artificial Intelligence (AI) have shown promising results in many areas and are driven by the increasing amount of available data. However, this data is o…
HyFed: A Hybrid Federated Framework for Privacy-preserving Machine Learning
Reza Nasirigerdeh, Reihaneh Torkzadehmahani, Julian Matschinske +3
Federated learning (FL) enables multiple clients to jointly train a global model under the coordination of a central server. Although FL is a privacy-aware paradigm, where raw data…
Flimma: a federated and privacy-preserving tool for differential gene expression analysis
Olga Zolotareva, Reza Nasirigerdeh, Julian Matschinske +9
Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights. As data exchange i…
Federated Multi-Mini-Batch: An Efficient Training Approach to Federated Learning in Non-IID Environments
Reza Nasirigerdeh, Mohammad Bakhtiari, Reihaneh Torkzadehmahani +4
Federated learning has faced performance and network communication challenges, especially in the environments where the data is not independent and identically distributed (IID) ac…
Privacy-preserving Artificial Intelligence Techniques in Biomedicine
Reihaneh Torkzadehmahani, Reza Nasirigerdeh, David B. Blumenthal +22
Artificial intelligence (AI) has been successfully applied in numerous scientific domains. In biomedicine, AI has already shown tremendous potential, e.g. in the interpretation of…
DP-CGAN: Differentially Private Synthetic Data and Label Generation
Reihaneh Torkzadehmahani, Peter Kairouz, Benedict Paten
Generative Adversarial Networks (GANs) are one of the well-known models to generate synthetic data including images, especially for research communities that cannot use original se…