HeteroSAg: Secure Aggregation With Heterogeneous Quantization in Federated Learning
Author:
Affiliation:
1. Department of Electrical Engineering, University of Southern California, Los Angeles, CA, USA
Funder
Defense Advanced Research Projects Agency
ARO
NSF
ONR
Intel/Avast via the PrivateAI Institute, and gifts from Qualcomm and Cisco
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/26/9758163/09712310.pdf?arnumber=9712310
Reference38 articles.
1. Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning
2. Membership Inference Attacks Against Machine Learning Models
3. Exploiting unintended feature leakage in collaborative learning;melis;arXiv 1805 04049,2018
4. Secure aggregation with heterogeneous quantization in federated learning;roushdy elkordy;arXiv 2009 14388,2020
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