Decentralized Stochastic Optimization with Inherent Privacy Protection

Yongqiang Wang, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

Abstract

Decentralized stochastic optimization is the basic building block of modern collaborative machine learning, distributed estimation and control, and large-scale sensing. Since involved data usually contain sensitive information like user locations, healthcare records and financial transactions, privacy protection has become an increasingly pressing need in the implementation of decentralized stochastic optimization algorithms. In this paper, we propose a decentralized stochastic gradient descent algorithm which is embedded with inherent privacy protection for every participating agent against other participating agents and external eavesdroppers. This proposed algorithm builds in a dynamics based gradient-obfuscation mechanism to enable privacy protection without compromising optimization accuracy, which is in significant difference from differential-privacy based privacy solutions for decentralized optimization that have to trade optimization accuracy for privacy.

Original languageEnglish (US)
Pages (from-to)1
Number of pages1
JournalIEEE Transactions on Automatic Control
DOIs
StateAccepted/In press - 2022

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

Keywords

  • Data privacy
  • Distributed databases
  • Estimation
  • Linear programming
  • Noise measurement
  • Optimization
  • Privacy

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