Presentation
Anomaly Detection with a Deep Abstaining Classifier Model Under Federated Learning
Presenter
DescriptionA deep abstaining classifier, or DAC, introduced first for combating label noise, is a regular deep neural network classifier (DNN) but with an additional (abstain) class and a custom loss function that permits abstention during training. This allows the DAC to identify and abstain on (or decline to classify) confusing samples, without the need for manually labeling these cases, while continuing to learn and improve classification performance on the non-abstained samples. It has been shown that the resulting models can significantly improve accuracy compared to the original DNN, at the cost of reduced coverage. The DAC learns patterns in the data that make prediction unreliable, is more robust to feature noise, and constitutes a useful tool to diagnose uncertain predictions at inference time. In this talk, we describe how we adapt the DAC to be combined with Federated Learning (FL) to allow for a distributed training using data from different silos that cannot be openly shared due to privacy concerns, as is frequently encountered when dealing with health records. We also demonstrate how this DAC+FL model can be applied for anomaly detection and discuss how to configure this per silo.
TimeTuesday, June 1715:30 - 16:00 CEST
LocationRoom 6.0D13
Session Chair
Event Type
Minisymposium
Applied Social Sciences and Humanities
Life Sciences
Computational Methods and Applied Mathematics