Privacy-Preserving Distributed Optimization and Learning

arXiv:2403.00157v1 Announce Type: new
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 …

abstract agent algorithms applications arxiv attention cs.cr cs.gt cs.lg development distributed information machine machine learning messages neighbors networks optimization privacy raise sensor smart type