A New Perspective on Privacy Protection in Federated Learning with Granular-Ball Computing

• URL: http://arxiv.org/abs/2501.04940v1
• Date: Thu, 09 Jan 2025 03:14:03 GMT
• Title: A New Perspective on Privacy Protection in Federated Learning with Granular-Ball Computing
• Authors: Guannan Lai, Yihui Feng, Xin Yang, Xiaoyu Deng, Hao Yu, Shuyin Xia, Guoyin Wang, Tianrui Li,
• Abstract summary: Federated Learning (FL) facilitates collaborative model training while prioritizing privacy by avoiding direct data sharing. We propose a novel framework called Granular-Ball Federated Learning (GrBFL) for image classification. GrBFL diverges from traditional methods that rely on the finest-grained input data. Instead, it segments images into multiple regions with optimal coarse granularity, which are then reconstructed into a graph structure.
• Score: 18.249608615824062
• License:
• Abstract: Federated Learning (FL) facilitates collaborative model training while prioritizing privacy by avoiding direct data sharing. However, most existing articles attempt to address challenges within the model's internal parameters and corresponding outputs, while neglecting to solve them at the input level. To address this gap, we propose a novel framework called Granular-Ball Federated Learning (GrBFL) for image classification. GrBFL diverges from traditional methods that rely on the finest-grained input data. Instead, it segments images into multiple regions with optimal coarse granularity, which are then reconstructed into a graph structure. We designed a two-dimensional binary search segmentation algorithm based on variance constraints for GrBFL, which effectively removes redundant information while preserving key representative features. Extensive theoretical analysis and experiments demonstrate that GrBFL not only safeguards privacy and enhances efficiency but also maintains robust utility, consistently outperforming other state-of-the-art FL methods. The code is available at https://github.com/AIGNLAI/GrBFL.

Related papers

• Vertical Federated Unlearning via Backdoor Certification [15.042986414487922]
  VFL offers a novel paradigm in machine learning, enabling distinct entities to train models cooperatively while maintaining data privacy. Recent privacy regulations emphasize an individual's emphright to be forgotten, which necessitates the ability for models to unlearn specific training data. We introduce an innovative modification to traditional VFL by employing a mechanism that inverts the typical learning trajectory with the objective of extracting specific data contributions.
  arXiv  Detail & Related papers  (2024-12-16T06:40:25Z)
• A New Federated Learning Framework Against Gradient Inversion Attacks [17.3044168511991]
  Federated Learning (FL) aims to protect data privacy by enabling clients to collectively train machine learning models without sharing their raw data. Recent studies demonstrate that information exchanged during FL is subject to Gradient Inversion Attacks (GIA)
  arXiv  Detail & Related papers  (2024-12-10T04:53:42Z)
• 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)
• GIFD: A Generative Gradient Inversion Method with Feature Domain Optimization [52.55628139825667]
  Federated Learning (FL) has emerged as a promising distributed machine learning framework to preserve clients' privacy. Recent studies find that an attacker can invert the shared gradients and recover sensitive data against an FL system by leveraging pre-trained generative adversarial networks (GAN) as prior knowledge. We propose textbfGradient textbfInversion over textbfFeature textbfDomains (GIFD), which disassembles the GAN model and searches the feature domains of the intermediate layers.
  arXiv  Detail & Related papers  (2023-08-09T04:34:21Z)
• Robust Split Federated Learning for U-shaped Medical Image Networks [16.046153872932653]
  We propose Robust Split Federated Learning (RoS-FL) for U-shaped medical image networks. RoS-FL is a novel hybrid learning paradigm of Federated Learning (FL) and Split Learning (SL)
  arXiv  Detail & Related papers  (2022-12-13T05:26:31Z)
• BlindFL: Vertical Federated Machine Learning without Peeking into Your Data [20.048695060411774]
  Vertical federated learning (VFL) describes a case where ML models are built upon the private data of different participated parties. We introduce BlindFL, a novel framework for VFL training and inference. We show that BlindFL supports diverse datasets and models efficiently whilst achieving robust privacy guarantees.
  arXiv  Detail & Related papers  (2022-06-16T07:26:50Z)
• Desirable Companion for Vertical Federated Learning: New Zeroth-Order Gradient Based Algorithm [140.25480610981504]
  A complete list of metrics to evaluate VFL algorithms should include model applicability, privacy, communication, and computation efficiency. We propose a novel VFL framework with black-box scalability, which is inseparably inseparably scalable.
  arXiv  Detail & Related papers  (2022-03-19T13:55:47Z)
• Do Gradient Inversion Attacks Make Federated Learning Unsafe? [70.0231254112197]
  Federated learning (FL) allows the collaborative training of AI models without needing to share raw data. Recent works on the inversion of deep neural networks from model gradients raised concerns about the security of FL in preventing the leakage of training data. In this work, we show that these attacks presented in the literature are impractical in real FL use-cases and provide a new baseline attack.
  arXiv  Detail & Related papers  (2022-02-14T18:33:12Z)
• Local Learning Matters: Rethinking Data Heterogeneity in Federated Learning [61.488646649045215]
  Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed learning with a network of clients (i.e., edge devices)
  arXiv  Detail & Related papers  (2021-11-28T19:03:39Z)
• Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating [159.48259714642447]
  Vertical scalable learning (VFL) attracts increasing attention due to the demands of multi-party collaborative modeling and concerns of privacy leakage. We propose a novel bftextlevel parallel architecture (VF$bfB2$), under which three new algorithms, including VF$B2$, are proposed.
  arXiv  Detail & Related papers  (2021-03-01T12:34:53Z)
• GRAFFL: Gradient-free Federated Learning of a Bayesian Generative Model [8.87104231451079]
  This paper presents the first gradient-free federated learning framework called GRAFFL. It uses implicit information derived from each participating institution to learn posterior distributions of parameters. We propose the GRAFFL-based Bayesian mixture model to serve as a proof-of-concept of the framework.
  arXiv  Detail & Related papers  (2020-08-29T07:19:44Z)

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.