Publication: The tsc- pfed architecture for privacy-preserving fl
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KU-Authors
KU Authors
Co-Authors
Truex, Stacey
Liu, Ling
Wei, Wenqi
Chow, Ka Ho
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Abstract
In this paper we will introduce our system for trust and (s) under bar eurity enhanced (c) under bar ustomizable (p) under bar rivate federated learning: TSC-PFed. We combine secure mUItiparty computation and differential privacy to allow participants to leverage known trust dynamics which allow for increased ML model accuracy while preserving privacy guarantees and introduce an update auditor to protect against malicious participants launching dangerous label Dipping data poisoning. We additionally introduce customizable modules into the TSC-PFed ecosystem which (a) allow users to customize the type of privacy protection provided and (b) provide a tiered participant selection approach which considers variation in privacy budgets.
Source
Publisher
IEEE Computer Soc
Subject
Computer science, Artificial intelligence, Information systems, Theory methods
Citation
Has Part
Source
2021 Third IEEE International Conference on Trust, Privacy and Security In Intelligent Systems and Applications (Tps-Isa 2021)
Book Series Title
Edition
DOI
10.1109/TPSISA52974.2021.00052