Federated Learning on Riemannian Manifolds with Differential Privacy
This work addresses privacy risks in distributed machine learning for applications involving sensitive data, representing an incremental advancement by combining existing techniques in a novel context.
The paper tackles the problem of protecting sensitive information in federated learning by proposing a private framework on Riemannian manifolds with differential privacy, achieving convergence and privacy guarantees as demonstrated through numerical simulations.
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, this is the first federated learning framework on Riemannian manifold with a privacy guarantee and convergence results. Numerical simulations are performed on synthetic and real-world datasets to showcase the efficacy of the proposed PriRFed approach.