Secure Aggregation Based on Cryptographic Schemes for Federated Learning

Organizer: Cryptography and Privacy Engineering Group (ENCRYPTO), Department of Computer Science, TU Darmstadt

Abstract

Secure aggregation consists of computing the sum of data collected from multiple sources without disclosing the individual inputs and has been found useful for various applications ranging from electronic voting to smart grid measurements and more recently, federated learning. This latter technology emerged as a new collaborative machine learning technology whereby multiple parties holding private data contribute to the training of a global machine learning model. In this talk, we will study the suitability of secure aggregation based on cryptographic schemes to federated learning. We will present the specific challenges raised by federated learning and further overview some recent solutions targeting some of these challenges namely, scalability, security and robustness.

Speaker Bio

Melek Önen is an associate professor in the Digital Security department at EURECOM (Sophia-Antipolis, France). Her research interests are applied cryptography, information security and privacy. She holds a PhD in Computer Science from École Nationale Supérieure des Télécommunications de Paris (ENST, 2005) and obtained her “Habilitation à Diriger les Recherches” in 2017. She was/is involved in many European and national French research projects.