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Expo Demonstration
Practical Deployment of Secure Federated Learning: Challenges, Opportunities and Solutions
Yi Zhou · Nathalie Baracaldo

Mon Nov 28 08:00 AM -- 10:00 AM & Mon Nov 28 12:00 PM -- 02:00 PM (PST) @ Ballroom C

Federated Learning (FL) is an emerging machine learning approach that allows multiple entities (a.k.a. parties or clients) to collaboratively train a machine learning model under the coordination of an aggregator (a.k.a server) without directly revealing their private training data. Due to privacy or regulatory constraints, many industry sectors, for example, healthcare, banking and retailers, have growing interests in employing FL to facilitate model training across multiple data centers. However, employing the basic version of federated learning usually is not good enough to meet privacy needs. In particular, not directly sharing raw training data does not guarantee full privacy protection, as there are lots of attacks on model parameters to learn sensitive information about training data, such as membership inference attacks, data reconstruction attacks, and property testing attacks, etc. These existing threats call for cryptographic techniques to further protect FL systems. In this demo, we will walk through the importance of protecting FL with cryptographic techniques, discuss high-level basics of fully homomorphic encryption (FHE) and present an end-to-end implementation of FHE in our IBM FL library. Moreover, we will conduct a comprehensive comparison of the possible communication and computation costs for deploying FHE in FL. To conclude, we will summarize the existing challenges and opportunities in securing FL.

Author Information

Yi Zhou (IBM Research)
Nathalie Baracaldo (IBM Research)

Nathalie Baracaldo leads the AI Security and Privacy Solutions team and is a Research Staff Member at IBM’s Almaden Research Center in San Jose, CA. Nathalie is passionate about delivering machine learning solutions that are highly accurate, withstand adversarial attacks and protect data privacy. Her team focuses on two main areas: federated learning, where models are trained without directly accessing training data and adversarial machine learning, where defenses are designed to withstand potential attacks to the machine learning pipeline. Nathalie is the primary investigator for the DARPA program Guaranteeing AI Robustness Against Deception (GARD), where AI security is investigated. Her team contributes to the Adversarial Robustness 360 Toolbox (ART). Nathalie is also the co-editor of the book: “Federated Learning: A Comprehensive Overview of Methods and Applications”, 2022 available in paper and as e-book in Springer, Apple books and Amazon. Nathalie's primary research interests lie at the intersection of information security, privacy and trust. As part of her work, she has also designed and implemented secure systems in the areas of cloud computing, Platform as a Service, secure data sharing and Internet of the Things. She has also contributed to projects to design scalable systems that monitor, manage performance and manage service level agreements in cloud environments. In 2020, Nathalie received the IBM Master Inventor distinction for her contributions to the IBM Intellectual Property and innovation. Nathalie also received the 2021 Corporate Technical Recognition, one of the highest recognitions provided to IBMers for breakthrough technical achievements that have led to notable market and industry success for IBM. This recognition was awarded for Nathalie's contribution to the Trusted AI initiative. Nathalie is associated Editor IEEE Transactions on Service Computing. Nathalie received her Ph.D. degree from the University of Pittsburgh in 2016. Her dissertation focused on preventing insider threats through the use of adaptive access control systems that integrate multiple sources of contextual information. Some of the topics that she has explored in the past include secure storage systems, privacy in online social networks, secure interoperability in distributed systems, risk management and trust evaluation. During her Ph.D. studies she received the 2014 Allen Kent Award for Outstanding Contributions to the Graduate Program in Information Science by the School of Information Sciences at the University of Pittsburgh. Nathalie also holds a master’s degree with Cum Laude distinction in computer sciences from the Universidad de los Andes, Colombia. Prior to that, she earned two undergraduate degrees in Computer Science and Industrial Engineering at the same university.

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