Robust Distributed Nonconvex Optimization Enabling Communication Acceleration and Privacy Protection
Zichong Ou, Jie Lu

TL;DR
This paper introduces a robust distributed nonconvex optimization algorithm that enhances privacy and communication efficiency by using noise obfuscation and acceleration techniques, with proven convergence and empirical validation.
Contribution
The paper proposes RPP, a novel privacy-preserving distributed optimization algorithm with noise robustness and accelerated convergence, achieving optimal communication complexity.
Findings
RPP converges to a stationary point at a sublinear rate.
The accelerated RPP achieves optimal communication complexity.
Numerical experiments confirm robustness and efficiency of the proposed methods.
Abstract
This paper addresses a distributed nonconvex optimization problem over multi-agent networks, where each agent exchanges its local information solely with its neighbors. Given that most existing distributed nonconvex optimization algorithms are susceptible to information leakage during inter agent communications, we propose a Robust Proximal Primal dual algorithm, referred to as RPP, to enhance the security of information transmission. In contrast to many existing approaches that directly transmit local variables throughout the network, we introduce carefully designed random noises to obfuscate sensitive local information. This not only preserves privacy but also demonstrates the noise robustness of our proposed algorithm. We establish a sublinear rate at which RPP converges to a stationary solution. Moreover, by incorporating Chebyshev acceleration, an accelerated variant of RPP is…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Stochastic Gradient Optimization Techniques · Distributed Control Multi-Agent Systems
