Privacy-Preserving Distributed Optimization and Learning

arXiv:2403.00157v1 Announce Type: cross
Abstract: Distributed optimization and learning has recently garnered great attention due to its wide applications in sensor networks, smart grids, machine learning, and so forth. Despite rapid development, existing distributed optimization and learning algorithms require each agent to exchange messages with its neighbors, which may expose sensitive information and raise significant privacy concerns. In this survey paper, we overview privacy-preserving distributed optimization and learning methods. We first discuss cryptography, differential privacy, and other techniques that can …

agent algorithms applications arxiv attention cs.cr cs.gt cs.lg development distributed exchange expose great information machine machine learning may messages neighbors networks optimization privacy rapid sensitive sensitive information sensor smart