AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning

• URL: http://arxiv.org/abs/2105.00395v1
• Date: Sun, 2 May 2021 05:45:43 GMT
• Title: AirMixML: Over-the-Air Data Mixup for Inherently Privacy-Preserving Edge Machine Learning
• Authors: Yusuke Koda and Jihong Park and Mehdi Bennis and Praneeth Vepakomma and Ramesh Raskar
• Abstract summary: We propose a privacy-preserving machine learning framework at the network edge, coined over-the-air mixup ML (AirMixML) In AirMixML, multiple workers transmit analog-modulated signals of their private data samples to an edge server who trains an ML model using the received noisy-and superpositioned samples. By simulations, we provide DirMix(alpha)-PC design guidelines to improve accuracy, privacy, and energy-efficiency.
• Score: 54.52660257575346
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Wireless channels can be inherently privacy-preserving by distorting the received signals due to channel noise, and superpositioning multiple signals over-the-air. By harnessing these natural distortions and superpositions by wireless channels, we propose a novel privacy-preserving machine learning (ML) framework at the network edge, coined over-the-air mixup ML (AirMixML). In AirMixML, multiple workers transmit analog-modulated signals of their private data samples to an edge server who trains an ML model using the received noisy-and superpositioned samples. AirMixML coincides with model training using mixup data augmentation achieving comparable accuracy to that with raw data samples. From a privacy perspective, AirMixML is a differentially private (DP) mechanism limiting the disclosure of each worker's private sample information at the server, while the worker's transmit power determines the privacy disclosure level. To this end, we develop a fractional channel-inversion power control (PC) method, {\alpha}-Dirichlet mixup PC (DirMix({\alpha})-PC), wherein for a given global power scaling factor after channel inversion, each worker's local power contribution to the superpositioned signal is controlled by the Dirichlet dispersion ratio {\alpha}. Mathematically, we derive a closed-form expression clarifying the relationship between the local and global PC factors to guarantee a target DP level. By simulations, we provide DirMix({\alpha})-PC design guidelines to improve accuracy, privacy, and energy-efficiency. Finally, AirMixML with DirMix({\alpha})-PC is shown to achieve reasonable accuracy compared to a privacy-violating baseline with neither superposition nor PC.

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)
• Privacy-Preserving Split Learning with Vision Transformers using Patch-Wise Random and Noisy CutMix [38.370923655357366]
  In computer vision, the vision transformer (ViT) has increasingly superseded the convolutional neural network (CNN) for improved accuracy and robustness. Split learning (SL) emerges as a viable solution, leveraging server-side resources to train ViTs while utilizing private data from distributed devices. We propose a novel privacy-preserving SL framework that injects Gaussian noise into smashed data and mixes randomly chosen patches of smashed data across clients, coined DP-CutMixSL.
  arXiv  Detail & Related papers  (2024-08-02T06:24:39Z)
• 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)
• 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)
• Differentially Private CutMix for Split Learning with Vision Transformer [42.47713044228984]
  Vision transformer (ViT) has started to outpace the conventional CNN in computer vision tasks. Considering privacy-preserving distributed learning with ViT, we propose DP-CutMixSL.
  arXiv  Detail & Related papers  (2022-10-28T08:33:29Z)
• BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning [0.0]
  We present BEAS, the first blockchain-based framework for N-party Federated Learning. It provides strict privacy guarantees of training data using gradient pruning. Anomaly detection protocols are used to minimize the risk of data-poisoning attacks. We also define a novel protocol to prevent premature convergence in heterogeneous learning environments.
  arXiv  Detail & Related papers  (2022-02-06T17:11:14Z)
• Stochastic Coded Federated Learning with Convergence and Privacy Guarantees [8.2189389638822]
  Federated learning (FL) has attracted much attention as a privacy-preserving distributed machine learning framework. This paper proposes a coded federated learning framework, namely coded federated learning (SCFL) to mitigate the straggler issue. We characterize the privacy guarantee by the mutual information differential privacy (MI-DP) and analyze the convergence performance in federated learning.
  arXiv  Detail & Related papers  (2022-01-25T04:43:29Z)
• Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones [57.468730437381076]
  We present a Semi-supervised Federated Learning (SSFL) framework for privacy-preserving UAV image recognition. There are significant differences in the number, features, and distribution of local data collected by UAVs using different camera modules. We propose an aggregation rule based on the frequency of the client's participation in training, namely the FedFreq aggregation rule.
  arXiv  Detail & Related papers  (2022-01-03T16:49:33Z)
• FedMix: Approximation of Mixup under Mean Augmented Federated Learning [60.503258658382]
  Federated learning (FL) allows edge devices to collectively learn a model without directly sharing data within each device. Current state-of-the-art algorithms suffer from performance degradation as the heterogeneity of local data across clients increases. We propose a new augmentation algorithm, named FedMix, which is inspired by a phenomenal yet simple data augmentation method, Mixup.
  arXiv  Detail & Related papers  (2021-07-01T06:14:51Z)
• XOR Mixup: Privacy-Preserving Data Augmentation for One-Shot Federated Learning [49.130350799077114]
  We develop a privacy-preserving XOR based mixup data augmentation technique, coined XorMixup. The core idea is to collect other devices' encoded data samples that are decoded only using each device's own data samples. XorMixFL achieves up to 17.6% higher accuracy than Vanilla FL under a non-IID MNIST dataset.
  arXiv  Detail & Related papers  (2020-06-09T09:43:41Z)

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.