Education Class 4
Title: Robustness against Poisoning Attacks in Centralized and Federated Deep Learning Scenarios: A Survey
Instructor: Farinaz Koushanfar, UC San Diego
Abstract: Deep Learning (DL) has been increasingly deployed in various real-world applications due to its unprecedented performance and automated capability of learning hidden representations. While DL can achieve high task performance, the training process of a DL model is both time and resource-consuming. Therefore, current supply chains of the DL models assume the customers obtain pre-trained Deep Neural Networks (DNNs) from the third-party providers that have sufficient computing power. In the centralized setting, the model designer trains the DL model using the local dataset. However, the collected training data may contain erroneous or poisoned data points. The model designer might craft malicious training samples and inject backdoors in the DL model distributed to the users. As a result, the user’s model will malfunction. In the federated learning setting, the cloud server aggregates local models trained on individual local datasets and updates the global model. In this scenario, the local client could poison the local training set and/or arbitrarily manipulate the local update. If the cloud server incorporates the malicious local gradients in model aggregation, the resulting global model will have degraded performance or backdoor behaviors. In this class, we present a comprehensive overview of contemporary data poisoning and model poisoning attacks against DL models in both centralized and federated learning scenarios. In addition, we review existing detection and defense techniques against various poisoning attacks.
Bio: Farinaz Koushanfar is a professor and Henry Booker Faculty Scholar in the Electrical and Computer Engineering (ECE) department at University of California San Diego (UCSD), where she is also the co-founder and co-director of the UCSD Center for Machine-Intelligence, Computing & Security (MICS). Her research addresses several aspects of efficient computing and embedded systems, with a focus on hardware and system security, real-time/energy-efficient big data analytics under resource constraints, design automation and synthesis for emerging applications, as well as practical privacy-preserving computing. Dr. Koushanfar is a fellow of the Kavli Foundation Frontiers of the National Academy of Engineering and a fellow of IEEE. She has received a number of awards and honors including the Presidential Early Career Award for Scientists and Engineers (PECASE) from President Obama, the ACM SIGDA Outstanding New Faculty Award, Cisco IoT Security Grand Challenge Award, MIT Technology Review TR-35, Qualcomm Innovation Awards, as well as Young Faculty/CAREER Awards from NSF, DARPA, ONR and ARO.