Federated Learning on Riemannian Manifolds with Differential Privacy

• URL: http://arxiv.org/abs/2404.10029v1
• Date: Mon, 15 Apr 2024 12:32:20 GMT
• Title: Federated Learning on Riemannian Manifolds with Differential Privacy
• Authors: Zhenwei Huang, Wen Huang, Pratik Jawanpuria, Bamdev Mishra,
• Abstract summary: A malicious adversary can potentially infer sensitive information through various means. We propose a generic private FL framework defined on the differential privacy (DP) technique. We analyze the privacy guarantee while establishing the convergence properties. Numerical simulations are performed on synthetic and real-world datasets to showcase the efficacy of the proposed PriRFed approach.
• Score: 8.75592575216789
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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, 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.

Related papers

• Convergent Differential Privacy Analysis for General Federated Learning: the $f$-DP Perspective [57.35402286842029]
  Federated learning (FL) is an efficient collaborative training paradigm with a focus on local privacy. differential privacy (DP) is a classical approach to capture and ensure the reliability of private protections.
  arXiv  Detail & Related papers  (2024-08-28T08:22:21Z)
• Accuracy-Privacy Trade-off in the Mitigation of Membership Inference Attack in Federated Learning [4.152322723065285]
  federated learning (FL) has emerged as a prominent method in machine learning, emphasizing privacy preservation by allowing multiple clients to collaboratively build a model while keeping their training data private. Despite this focus on privacy, FL models are susceptible to various attacks, including membership inference attacks (MIAs)
  arXiv  Detail & Related papers  (2024-07-26T22:44:41Z)
• Universally Harmonizing Differential Privacy Mechanisms for Federated Learning: Boosting Accuracy and Convergence [22.946928984205588]
  Differentially private federated learning (DP-FL) is a promising technique for collaborative model training. We propose the first DP-FL framework (namely UDP-FL) which universally harmonizes any randomization mechanism. We show that UDP-FL exhibits substantial resilience against different inference attacks.
  arXiv  Detail & Related papers  (2024-07-20T00:11:59Z)
• QMGeo: Differentially Private Federated Learning via Stochastic Quantization with Mixed Truncated Geometric Distribution [1.565361244756411]
  Federated learning (FL) is a framework which allows multiple users to jointly train a global machine learning (ML) model. One key motivation of such distributed frameworks is to provide privacy guarantees to the users. We present a novel quantization method, utilizing a mixed geometric distribution to introduce the randomness needed to provide DP.
  arXiv  Detail & Related papers  (2023-12-10T04:44:53Z)
• Factor-Assisted Federated Learning for Personalized Optimization with Heterogeneous Data [6.024145412139383]
  Federated learning is an emerging distributed machine learning framework aiming at protecting data privacy. Data in different clients contain both common knowledge and personalized knowledge. We develop a novel personalized federated learning framework for heterogeneous data, which we refer to as FedSplit.
  arXiv  Detail & Related papers  (2023-12-07T13:05:47Z)
• Privacy-preserving Federated Primal-dual Learning for Non-convex and Non-smooth Problems with Model Sparsification [51.04894019092156]
  Federated learning (FL) has been recognized as a rapidly growing area, where the model is trained over clients under the FL orchestration (PS) In this paper, we propose a novel primal sparification algorithm for and guarantee non-smooth FL problems. Its unique insightful properties and its analyses are also presented.
  arXiv  Detail & Related papers  (2023-10-30T14:15:47Z)
• Differentially Private Wireless Federated Learning Using Orthogonal Sequences [56.52483669820023]
  We propose a privacy-preserving uplink over-the-air computation (AirComp) method, termed FLORAS. We prove that FLORAS offers both item-level and client-level differential privacy guarantees. A new FL convergence bound is derived which, combined with the privacy guarantees, allows for a smooth tradeoff between the achieved convergence rate and differential privacy levels.
  arXiv  Detail & Related papers  (2023-06-14T06:35:10Z)
• PS-FedGAN: An Efficient Federated Learning Framework Based on Partially Shared Generative Adversarial Networks For Data Privacy [56.347786940414935]
  Federated Learning (FL) has emerged as an effective learning paradigm for distributed computation. This work proposes a novel FL framework that requires only partial GAN model sharing. Named as PS-FedGAN, this new framework enhances the GAN releasing and training mechanism to address heterogeneous data distributions.
  arXiv  Detail & Related papers  (2023-05-19T05:39:40Z)
• Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy [67.4471689755097]
  This paper empirically demonstrates that the clipped FedAvg can perform surprisingly well even with substantial data heterogeneity. We provide the convergence analysis of a differential private (DP) FedAvg algorithm and highlight the relationship between clipping bias and the distribution of the clients' updates.
  arXiv  Detail & Related papers  (2021-06-25T14:47:19Z)
• Differentially Private Federated Learning with Laplacian Smoothing [72.85272874099644]
  Federated learning aims to protect data privacy by collaboratively learning a model without sharing private data among users. An adversary may still be able to infer the private training data by attacking the released model. Differential privacy provides a statistical protection against such attacks at the price of significantly degrading the accuracy or utility of the trained models.
  arXiv  Detail & Related papers  (2020-05-01T04:28:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.