Decentralized Stochastic Optimization with Inherent Privacy Protection
Yongqiang Wang, H. Vincent Poor

TL;DR
This paper introduces a decentralized stochastic gradient descent algorithm that inherently protects privacy through a dynamics-based gradient obfuscation mechanism, avoiding the trade-offs of traditional differential privacy methods.
Contribution
The paper presents a novel privacy-preserving decentralized stochastic gradient descent algorithm with a dynamics-based mechanism that maintains accuracy without heavy encryption or privacy-accuracy trade-offs.
Findings
Proves convergence under convex and non-convex objectives.
Provides information-theoretic privacy guarantees.
Demonstrates effectiveness through simulations and experiments.
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…
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Taxonomy
TopicsStochastic Gradient Optimization Techniques · Privacy-Preserving Technologies in Data · Age of Information Optimization
