DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning using Packed Secret Sharing

• URL: http://arxiv.org/abs/2410.16161v1
• Date: Mon, 21 Oct 2024 16:25:14 GMT
• Title: DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning using Packed Secret Sharing
• Authors: Alexander Bienstock, Ujjwal Kumar, Antigoni Polychroniadou,
• Abstract summary: 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.
• Score: 51.336015600778396
• License:
• Abstract: 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. Differential Privacy (DP) has become the main measure of privacy in the FL setting. DP comes in two flavors: central and local. In the former, a centralized server is trusted to receive the users' raw gradients from a training step, and then perturb their aggregation with some noise before releasing the next version of the model. In the latter (more private) setting, noise is applied on users' local devices, and only the aggregation of users' noisy gradients is revealed even to the server. Great strides have been made in increasing the privacy-utility trade-off in the central DP setting, by utilizing the so-called matrix mechanism. However, progress has been mostly stalled in the local DP setting. In this work, we introduce the distributed matrix mechanism to achieve the best-of-both-worlds; local DP and also better privacy-utility trade-off from the matrix mechanism. We accomplish this by proposing a cryptographic protocol that securely transfers sensitive values across rounds, which makes use of packed secret sharing. This protocol accommodates the dynamic participation of users per training round required by FL, including those that may drop out from the computation. We provide experiments which show that our mechanism indeed significantly improves the privacy-utility trade-off of FL models compared to previous local DP mechanisms, with little added overhead.

Related papers

• DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation [15.023077875990614]
  Federated learning (FL) allows clients to collaboratively train a global model without sharing their local data with a server. Differential privacy (DP) addresses such leakage by providing formal privacy guarantees, with mechanisms that add randomness to the clients' contributions. We propose an adaptation method that can be combined with differential privacy and call it DP-DyLoRA.
  arXiv  Detail & Related papers  (2024-05-10T10:10:37Z)
• 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)
• Differentially Private Over-the-Air Federated Learning Over MIMO Fading Channels [24.534729104570417]
  Federated learning (FL) enables edge devices to collaboratively train machine learning models. While over-the-air model aggregation improves communication efficiency, uploading models to an edge server over wireless networks can pose privacy risks. We show that FL model communication with a multiple-antenna server amplifies privacy leakage.
  arXiv  Detail & Related papers  (2023-06-19T14:44:34Z)
• Federated Nearest Neighbor Machine Translation [66.8765098651988]
  In this paper, we propose a novel federated nearest neighbor (FedNN) machine translation framework. FedNN leverages one-round memorization-based interaction to share knowledge across different clients. Experiments show that FedNN significantly reduces computational and communication costs compared with FedAvg.
  arXiv  Detail & Related papers  (2023-02-23T18:04:07Z)
• Federated Learning with Privacy-Preserving Ensemble Attention Distillation [63.39442596910485]
  Federated Learning (FL) is a machine learning paradigm where many local nodes collaboratively train a central model while keeping the training data decentralized. We propose a privacy-preserving FL framework leveraging unlabeled public data for one-way offline knowledge distillation. Our technique uses decentralized and heterogeneous local data like existing FL approaches, but more importantly, it significantly reduces the risk of privacy leakage.
  arXiv  Detail & Related papers  (2022-10-16T06:44:46Z)
• Hierarchical Federated Learning with Privacy [22.392087447002567]
  Federated learning (FL) where data remains at the federated clients, and where only gradient updates are shared with a central aggregator, was assumed to be private. Recent work demonstrates that adversaries with gradient-level access can mount successful inference and reconstruction attacks. In this work, we take the first step towards mitigating such trade-offs through em hierarchical FL (HFL).
  arXiv  Detail & Related papers  (2022-06-10T16:10:42Z)
• Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence [73.14373832423156]
  We propose DP-Sinkhorn, a novel optimal transport-based generative method for learning data distributions from private data with differential privacy. Unlike existing approaches for training differentially private generative models, we do not rely on adversarial objectives.
  arXiv  Detail & Related papers  (2021-11-01T18:10:21Z)
• 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)
• Privacy Amplification via Random Check-Ins [38.72327434015975]
  Differentially Private Gradient Descent (DP-SGD) forms a fundamental building block in many applications for learning over sensitive data. In this paper, we focus on conducting iterative methods like DP-SGD in the setting of federated learning (FL) wherein the data is distributed among many devices (clients) Our main contribution is the emphrandom check-in distributed protocol, which crucially relies only on randomized participation decisions made locally and independently by each client.
  arXiv  Detail & Related papers  (2020-07-13T18:14:09Z)
• 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.