Federated Learning on Riemannian Manifolds with Differential Privacy

arXiv:2404.10029v1 Announce Type: cross
Abstract: In recent years, federated learning (FL) has emerged as a prominent paradigm in distributed machine learning. Despite the partial safeguarding of agents' information within FL systems, a malicious adversary can potentially infer sensitive information through various means. In this paper, we propose a generic private FL framework defined on Riemannian manifolds (PriRFed) based on the differential privacy (DP) technique. We analyze the privacy guarantee while establishing the convergence properties. To the best of our knowledge, …

adversary agents arxiv can cs.cr cs.lg differential privacy distributed federated federated learning framework information machine machine learning malicious math.oc paradigm partial privacy private sensitive sensitive information systems