TH3.R3.2

Secure Inference for Vertically Partitioned Data Using Multiparty Homomorphic Encryption

Shuangyi Chen, University of Toronto, Canada; Yue Ju, Zhongwen Zhu, Ericsson, Canada; Ashish Khisti, University of Toronto, Canada
Session:
Secure Federated Learning
Track:
15: Distributed and Federated Learning
Location:
Ypsilon IV-V-VI
Presentation Time:
Thu, 11 Jul, 14:55 - 15:15
Session Chair:
Namrata Vaswani, Iowa State University
Presentation
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Discussion
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Session TH3.R3
TH3.R3.1: VALID: a Validated Algorithm for Learning in Decentralized Networks with Possible Adversarial Presence
Mayank Bakshi, Arizona State University, United States; Sara Ghasvarianjahromi, Yauhen Yakimenka, New Jersey Institute of Technology, United States; Allison Beemer, University of Wisconsin-Eau Claire, United States; Oliver Kosut, Arizona State University, United States; Joerg Kliewer, New Jersey Institute of Technology, United States
TH3.R3.2: Secure Inference for Vertically Partitioned Data Using Multiparty Homomorphic Encryption
Shuangyi Chen, University of Toronto, Canada; Yue Ju, Zhongwen Zhu, Ericsson, Canada; Ashish Khisti, University of Toronto, Canada
TH3.R3.3: Byzantine-Resilient Federated Principal Subspace Estimation
Ankit Pratap Singh, Namrata Vaswani, Iowa State University, United States
TH3.R3.4: Robust Distributed Gradient Descent to Corruption over Noisy Channels
Shuche Wang, Vincent Y. F. Tan, National University of Singapore, Singapore
Resources
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