Federated Transfer Learning with Differential Privacy

• URL: http://arxiv.org/abs/2403.11343v2
• Date: Tue, 9 Apr 2024 10:34:40 GMT
• Title: Federated Transfer Learning with Differential Privacy
• Authors: Mengchu Li, Ye Tian, Yang Feng, Yi Yu,
• Abstract summary: We formulate the notion of textitfederated differential privacy, which offers privacy guarantees for each data set without assuming a trusted central server. We show that federated differential privacy is an intermediate privacy model between the well-established local and central models of differential privacy.
• Score: 21.50525027559563
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Federated learning is gaining increasing popularity, with data heterogeneity and privacy being two prominent challenges. In this paper, we address both issues within a federated transfer learning framework, aiming to enhance learning on a target data set by leveraging information from multiple heterogeneous source data sets while adhering to privacy constraints. We rigorously formulate the notion of \textit{federated differential privacy}, which offers privacy guarantees for each data set without assuming a trusted central server. Under this privacy constraint, we study three classical statistical problems, namely univariate mean estimation, low-dimensional linear regression, and high-dimensional linear regression. By investigating the minimax rates and identifying the costs of privacy for these problems, we show that federated differential privacy is an intermediate privacy model between the well-established local and central models of differential privacy. Our analyses incorporate data heterogeneity and privacy, highlighting the fundamental costs of both in federated learning and underscoring the benefit of knowledge transfer across data sets.

Related papers

• Tabular Data Synthesis with Differential Privacy: A Survey [24.500349285858597]
  Data sharing is a prerequisite for collaborative innovation, enabling organizations to leverage diverse datasets for deeper insights. Data synthesis tackles this by generating artificial datasets that preserve the statistical characteristics of real data. Differentially private data synthesis has emerged as a promising approach to privacy-aware data sharing.
  arXiv  Detail & Related papers  (2024-11-04T06:32:48Z)
• FewFedPIT: Towards Privacy-preserving and Few-shot Federated Instruction Tuning [54.26614091429253]
  Federated instruction tuning (FedIT) is a promising solution, by consolidating collaborative training across multiple data owners. FedIT encounters limitations such as scarcity of instructional data and risk of exposure to training data extraction attacks. We propose FewFedPIT, designed to simultaneously enhance privacy protection and model performance of federated few-shot learning.
  arXiv  Detail & Related papers  (2024-03-10T08:41:22Z)
• Federated Learning Empowered by Generative Content [55.576885852501775]
  Federated learning (FL) enables leveraging distributed private data for model training in a privacy-preserving way. We propose a novel FL framework termed FedGC, designed to mitigate data heterogeneity issues by diversifying private data with generative content. We conduct a systematic empirical study on FedGC, covering diverse baselines, datasets, scenarios, and modalities.
  arXiv  Detail & Related papers  (2023-12-10T07:38:56Z)
• A Unified View of Differentially Private Deep Generative Modeling [60.72161965018005]
  Data with privacy concerns comes with stringent regulations that frequently prohibited data access and data sharing. Overcoming these obstacles is key for technological progress in many real-world application scenarios that involve privacy sensitive data. Differentially private (DP) data publishing provides a compelling solution, where only a sanitized form of the data is publicly released.
  arXiv  Detail & Related papers  (2023-09-27T14:38:16Z)
• How Do Input Attributes Impact the Privacy Loss in Differential Privacy? [55.492422758737575]
  We study the connection between the per-subject norm in DP neural networks and individual privacy loss. We introduce a novel metric termed the Privacy Loss-Input Susceptibility (PLIS) which allows one to apportion the subject's privacy loss to their input attributes.
  arXiv  Detail & Related papers  (2022-11-18T11:39:03Z)
• Momentum Gradient Descent Federated Learning with Local Differential Privacy [10.60240656423935]
  In the big data era, the privacy of personal information has been more pronounced. In this article, we propose integrating federated learning and local differential privacy with momentum gradient descent to improve the performance of machine learning models.
  arXiv  Detail & Related papers  (2022-09-28T13:30:38Z)
• Mixed Differential Privacy in Computer Vision [133.68363478737058]
  AdaMix is an adaptive differentially private algorithm for training deep neural network classifiers using both private and public image data. A few-shot or even zero-shot learning baseline that ignores private data can outperform fine-tuning on a large private dataset.
  arXiv  Detail & Related papers  (2022-03-22T06:15:43Z)
• Federated $f$-Differential Privacy [19.499120576896228]
  Federated learning (FL) is a training paradigm where the clients collaboratively learn models by repeatedly sharing information. We introduce federated $f$-differential privacy, a new notion specifically tailored to the federated setting. We then propose a generic private federated learning framework PriFedSync that accommodates a large family of state-of-the-art FL algorithms.
  arXiv  Detail & Related papers  (2021-02-22T16:28:21Z)
• Robustness Threats of Differential Privacy [70.818129585404]
  We experimentally demonstrate that networks, trained with differential privacy, in some settings might be even more vulnerable in comparison to non-private versions. We study how the main ingredients of differentially private neural networks training, such as gradient clipping and noise addition, affect the robustness of the model.
  arXiv  Detail & Related papers  (2020-12-14T18:59:24Z)
• LDP-FL: Practical Private Aggregation in Federated Learning with Local Differential Privacy [20.95527613004989]
  Federated learning is a popular approach for privacy protection that collects the local gradient information instead of real data. Previous works do not give a practical solution due to three issues. Last, the privacy budget explodes due to the high dimensionality of weights in deep learning models.
  arXiv  Detail & Related papers  (2020-07-31T01:08:57Z)
• Privately Learning Markov Random Fields [44.95321417724914]
  We consider the problem of learning Random Fields (including the Ising model) under the constraint of differential privacy. We provide algorithms and lower bounds for both problems under a variety of privacy constraints.
  arXiv  Detail & Related papers  (2020-02-21T18:30:48Z)

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.