A Weighted Gradient Tracking Privacy-Preserving Method for Distributed Optimization
This paper investigates the privacy-preserving distributed optimization problem, aiming to protect agents' private information from potential attackers during the optimization process. Gradient tracking, an advanced technique for improving the convergence rate in distributed optimization, has been applied to most first-order algorithms in recent years. We first reveal the inherent privacy leakage risk associated with gradient tracking. Building upon this insight, we propose a weighted gradient tracking distributed privacy-preserving algorithm, eliminating the privacy leakage risk in gradient tracking using decaying weight factors. Then, we characterize the convergence of the proposed algorithm under time-varying heterogeneous step sizes. We prove the proposed algorithm converges precisely to the optimal solution under mild assumptions. Finally, numerical simulations validate the algorithm's effectiveness through a classical distributed estimation problem and the distributed training of a convolutional neural network.
Code (0)
등록된 구현이 없습니다.
Tasks
Distributed OptimizationSimilar Papers 제목 키워드 기반
Private Weighted Random Walk Stochastic Gradient Descent
We consider a decentralized learning setting in which data is distributed over nodes in a graph. The goal is to learn a global model on the distributed data without involving any central entity that needs to be trusted. …
Privacy PreservingWeighted Distributed Differential Privacy ERM: Convex and Non-convex
Distributed machine learning is an approach allowing different parties to learn a model over all data sets without disclosing their own data. In this paper, we propose a weighted distributed differential privacy (WD-DP) …
Differentially Private Gradient-Tracking-Based Distributed Stochastic Optimization over Directed Graphs
This paper proposes a new differentially private gradient-tracking-based distributed stochastic optimization algorithm over directed graphs. Specifically, privacy noises are added to each agent's state and tracking varia…
Stochastic OptimizationPrivacy-preserving Pedestrian Tracking using Distributed 3D LiDARs
The growing demand for intelligent environments unleashes an extraordinary cycle of privacy-aware applications that makes individuals' life more comfortable and safe. Examples of these applications include pedestrian tra…
Metric LearningMulti-Object TrackingPrivacy PreservingPrivacy-Preserving Distributed Learning in IoT Systems: A Unified Threat Model and Evaluation Framework
The increasing deployment of Internet-of-Things (IoT) devices has accelerated the use of distributed learning frameworks, where data remains local while model updates are shared across decentralized systems. Although thi…