Belt and Braces: When Federated Learning Meets Differential Privacy

• URL: http://arxiv.org/abs/2404.18814v2
• Date: Wed, 23 Oct 2024 11:17:12 GMT
• Title: Belt and Braces: When Federated Learning Meets Differential Privacy
• Authors: Xuebin Ren, Shusen Yang, Cong Zhao, Julie McCann, Zongben Xu,
• Abstract summary: Federated learning (FL) has great potential for large-scale machine learning (ML) without exposing raw data. Differential privacy (DP) is the de facto standard of privacy protection with provable guarantees. Practitioners often not only are not fully aware of its development and categorization, but also face a hard choice between privacy and utility.
• Score: 22.116742377692518
• License:
• Abstract: Federated learning (FL) has great potential for large-scale machine learning (ML) without exposing raw data.Differential privacy (DP) is the de facto standard of privacy protection with provable guarantees.Advances in ML suggest that DP would be a perfect fit for FL with comprehensive privacy preservation. Hence, extensive efforts have been devoted to achieving practically usable FL with DP, which however is still challenging.Practitioners often not only are not fully aware of its development and categorization, but also face a hard choice between privacy and utility. Therefore, it calls for a holistic review of current advances and an investigation on the challenges and opportunities for highly usable FL systems with a DP guarantee. In this article, we first introduce the primary concepts of FL and DP, and highlight the benefits of integration. We then review the current developments by categorizing different paradigms and notions. Aiming at usable FL with DP, we present the optimization principles to seek a better tradeoff between model utility and privacy loss. Finally, we discuss future challenges in the emergent areas and relevant research topics.

Related papers

• DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning using Packed Secret Sharing [51.336015600778396]
  Federated Learning (FL) has gained lots of traction recently, both in industry and academia. In FL, a machine learning model is trained using data from various end-users arranged in committees across several rounds. Since such data can often be sensitive, a primary challenge in FL is providing privacy while still retaining utility of the model.
  arXiv  Detail & Related papers  (2024-10-21T16:25:14Z)
• 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)
• 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)
• Mind the Privacy Unit! User-Level Differential Privacy for Language Model Fine-Tuning [62.224804688233]
  differential privacy (DP) offers a promising solution by ensuring models are 'almost indistinguishable' with or without any particular privacy unit. We study user-level DP motivated by applications where it necessary to ensure uniform privacy protection across users.
  arXiv  Detail & Related papers  (2024-06-20T13:54:32Z)
• 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)
• A Survey of Federated Unlearning: A Taxonomy, Challenges and Future Directions [71.16718184611673]
  The evolution of privacy-preserving Federated Learning (FL) has led to an increasing demand for implementing the right to be forgotten. The implementation of selective forgetting is particularly challenging in FL due to its decentralized nature. Federated Unlearning (FU) emerges as a strategic solution to address the increasing need for data privacy.
  arXiv  Detail & Related papers  (2023-10-30T01:34:33Z)
• ULDP-FL: Federated Learning with Across Silo User-Level Differential Privacy [19.017342515321918]
  Differentially Private Federated Learning (DP-FL) has garnered attention as a collaborative machine learning approach that ensures formal privacy. We present Uldp-FL, a novel FL framework designed to guarantee user-level DP in cross-silo FL where a single user's data may belong to multiple silos.
  arXiv  Detail & Related papers  (2023-08-23T15:50:51Z)
• How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy [22.906644117887133]
  Differential Privacy (DP) has become a gold standard for making formal statements about data anonymization. The adoption of DP is hindered by limited practical guidance of what DP protection entails, what privacy guarantees to aim for, and the difficulty of achieving good privacy-utility-computation trade-offs for ML models. This work is a self-contained guide that gives an in-depth overview of the field of DP ML and presents information about achieving the best possible DP ML model with rigorous privacy guarantees.
  arXiv  Detail & Related papers  (2023-03-01T16:56:39Z)
• Differentially Private Federated Learning on Heterogeneous Data [10.431137628048356]
  Federated Learning (FL) is a paradigm for large-scale distributed learning. It faces two key challenges: (i) efficient training from highly heterogeneous user data, and (ii) protecting the privacy of participating users. We propose a novel FL approach to tackle these two challenges together by incorporating Differential Privacy (DP) constraints.
  arXiv  Detail & Related papers  (2021-11-17T18:23:49Z)
• Differentially Private Federated Bayesian Optimization with Distributed Exploration [48.9049546219643]
  We introduce differential privacy (DP) into the training of deep neural networks through a general framework for adding DP to iterative algorithms. We show that DP-FTS-DE achieves high utility (competitive performance) with a strong privacy guarantee. We also use real-world experiments to show that DP-FTS-DE induces a trade-off between privacy and utility.
  arXiv  Detail & Related papers  (2021-10-27T04:11:06Z)
• Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization [27.243322019117144]
  Federated learning (FL) enables distributed agents to collaboratively learn a centralized model without sharing their raw data with each other. We propose a new FL framework with sparsification-amplified privacy. Our approach integrates random sparsification with gradient perturbation on each agent to amplify privacy guarantee.
  arXiv  Detail & Related papers  (2020-08-01T20:22:57Z)

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.