Collaborative Research: SaTC: CORE: Medium: Towards Secure Federated Learning

Duke University, Durham NC

Investigators

Abstract

This project will provide the security foundations for the emerging paradigm of federated learning. Federated learning has seen large-scale deployment in diverse societal applications because it enables many clients (e.g., smartphones, IoT devices, and edge devices) to collaboratively learn from a machine learning model. With help of a cloud server, the process allows analysis without having to share private data. While there are already many studies on improving the accuracy and communication efficiency of federated learning, its security is much less explored. In this project, the investigators will bridge the gap by exploring new security attacks to federated learning and developing new secure federated learning methods that reduce the risk that the analyses and models can be manipulated by outside actors. This project has three objectives targeting the security of federated learning. First, the research team will systematically investigate the security vulnerabilities of federated learning. In particular, they will explore security vulnerabilities in the training phase of federated learning, such as poisoning attacks and backdoor attacks. Second, the team will develop provably secure federated learning methods to prevent poisoning attacks and backdoor attacks. Specifically, methods will be developed that ensure a bounded number of malicious clients cannot attack the machine learning model in a provably secure federated learning method no matter what poisoning and backdoor attacks they use. Third, the team of researchers will develop methods to detect malicious clients and efficiently recover a machine learning model from attacks. The investigators will aim for real-world technology transfer, incorporate the results of this project in both new and existing undergraduate and graduate courses, and develop and train undergraduate and graduate researchers with significant experience for developing secure federated learning systems, including recruiting minority and under-represented students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.