Have you forgotten? A method to assess if machine learning models have forgotten data

In the era of deep learning, aggregation of data from several sources is a common approach to ensuring data diversity. Let us consider a scenario where several providers contribute data to a consortium for the joint development of a classification model (hereafter the target model), but, now one of the providers decides to leave. This provider requests that their data (hereafter the query dataset) be removed from the databases but also that the model `forgets' their data. In this paper, for the first time, we want to address the challenging question of whether data have been forgotten by a model. We assume knowledge of the query dataset and the distribution of a model's output. We establish statistical methods that compare the target's outputs with outputs of models trained with different datasets. We evaluate our approach on several benchmark datasets (MNIST, CIFAR-10 and SVHN) and on a cardiac pathology diagnosis task using data from the Automated Cardiac Diagnosis Challenge (ACDC). We hope to encourage studies on what information a model retains and inspire extensions in more complex settings.Comment: Accepted by MICCAI 202

Demanding business travel:the evolution of the timespaces of business practice

To date, virtual ways of working have yet to substantially reduce demand for business travel. Emerging research claims that virtual and physical work compliment rather than substitute for one another. This suggests travel demand stems from business strategies and achieving business outcomes. In building on these ideas, this chapter draws upon Schatzki’s conception of timespace to capture changes in how two UK-based global construction and engineering consulting firms organise work and the implications in terms of demand for business travel. Overtime, particular forms of spatially stretched organisation which have developed are found to require the interweaving of timespaces through travel. As such, how each firm has evolved has in turn created the contemporary situation of significant and hard to reduce demand for travel

Multi-diseases Classification from Chest-X-ray: A Federated Deep Learning Approach

Data plays a vital role in deep learning model training. In large-scale medical image analysis, data privacy and ownership make data gathering challenging in a centralized location. Hence, federated learning has been shown as successful in alleviating both problems for the last few years. In this work, we have proposed multi-diseases classification from chest-X-ray using Federated Deep Learning (FDL). The FDL approach detects pneumonia from chest-X-ray and also identifies viral and bacterial pneumonia. Without submitting the chest-X-ray images to a central server, clients train the local models with limited private data at the edge server and send them to the central server for global aggregation. We have used four pre-trained models such as ResNet18, ResNet50, DenseNet121, and MobileNetV2, and applied transfer learning on them at each edge server. The learned models in the federated setting have compared with centrally trained deep learning models. It has been observed that the models trained using the ResNet18 in a federated environment produce accuracy up to 98.3%98.3% for pneumonia detection and up to 87.3% accuracy for viral and bacterial pneumonia detection. We have compared the performance of adaptive learning rate based optimizers such as Adam and Adamax with Momentum based Stochastic Gradient Descent (SGD) and found out that Momentum SGD yields better results than others. Lastly, for visualization, we have used Class Activation Mapping (CAM) approaches such as Grad-CAM, Grad-CAM++, and Score-CAM to identify pneumonia affected regions in a chest-X-ray.</p