Towards Zero-Trust Federated Learning
Department of Electrical and Computer Engineering
Location: Babbio 310
Speaker: Jiarui Li, Ph.D. Candidate, Electrical and Computer Engineering Department, Stevens Institute of Technology
ABSTRACT
Federated Learning, as a promising distributed machine learning approach that allows collaborative model training without sharing raw data, has gained prominence as a key application in zero-trust edge computing. However, the decentralized nature of FL poses challenges in ensuring the integrity of the training process, as malicious participants can undermine the global model’s accuracy and reliability. In this talk, I will describe my recent work on the hardware-assisted integrity verification framework for Federated Learning that ensures the integrity of training participants without sacrificing their privacy.
BIOGRAPHY
Jiarui Li is currently a Ph.D. candidate in the Department of Electrical and Computer Engineering at Stevens Institute of Technology. His research interests include applied cryptography, trustworthy AI, and post-quantum cryptography. Jiarui received the B.S. degree from South China University of Technology (SCUT) in 2017, and the M.S. degree from Stevens Institute of Technology in 2019.
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