Self-supervised On-device Federated Learning from Unlabeled Streams
- URL: http://arxiv.org/abs/2212.01006v2
- Date: Thu, 25 May 2023 12:13:10 GMT
- Title: Self-supervised On-device Federated Learning from Unlabeled Streams
- Authors: Jiahe Shi, Yawen Wu, Dewen Zeng, Jun Tao, Jingtong Hu, Yiyu Shi
- Abstract summary: We propose a Self-supervised On-device Federated learning framework with coreset selection, which we call SOFed, to automatically select a coreset.
Experiments demonstrate the effectiveness and significance of the proposed method in visual representation learning.
- Score: 15.94978097767473
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The ubiquity of edge devices has led to a growing amount of unlabeled data
produced at the edge. Deep learning models deployed on edge devices are
required to learn from these unlabeled data to continuously improve accuracy.
Self-supervised representation learning has achieved promising performances
using centralized unlabeled data. However, the increasing awareness of privacy
protection limits centralizing the distributed unlabeled image data on edge
devices. While federated learning has been widely adopted to enable distributed
machine learning with privacy preservation, without a data selection method to
efficiently select streaming data, the traditional federated learning framework
fails to handle these huge amounts of decentralized unlabeled data with limited
storage resources on edge. To address these challenges, we propose a
Self-supervised On-device Federated learning framework with coreset selection,
which we call SOFed, to automatically select a coreset that consists of the
most representative samples into the replay buffer on each device. It preserves
data privacy as each client does not share raw data while learning good visual
representations. Experiments demonstrate the effectiveness and significance of
the proposed method in visual representation learning.
Related papers
- Navigating Data Heterogeneity in Federated Learning A Semi-Supervised
Federated Object Detection [3.7398615061365206]
Federated Learning (FL) has emerged as a potent framework for training models across distributed data sources.
It faces challenges with limited high-quality labels and non-IID client data, particularly in applications like autonomous driving.
We present a pioneering SSFOD framework, designed for scenarios where labeled data reside only at the server while clients possess unlabeled data.
arXiv Detail & Related papers (2023-10-26T01:40:28Z) - Joint Optimization in Edge-Cloud Continuum for Federated Unsupervised
Person Re-identification [24.305773593017932]
FedUReID is a federated unsupervised person ReID system to learn person ReID models without any labels while preserving privacy.
To tackle the problem that edges vary in data volumes and distributions, we personalize training in edges with joint optimization of cloud and edge.
Experiments on eight person ReID datasets demonstrate that FedUReID achieves higher accuracy but also reduces computation cost by 29%.
arXiv Detail & Related papers (2021-08-14T08:35:55Z) - Enabling On-Device Self-Supervised Contrastive Learning With Selective
Data Contrast [13.563747709789387]
We propose a framework to automatically select the most representative data from the unlabeled input stream.
Experiments show that accuracy and learning speed are greatly improved.
arXiv Detail & Related papers (2021-06-07T17:04:56Z) - ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for
Semi-supervised Continual Learning [52.831894583501395]
Continual learning assumes the incoming data are fully labeled, which might not be applicable in real applications.
We propose deep Online Replay with Discriminator Consistency (ORDisCo) to interdependently learn a classifier with a conditional generative adversarial network (GAN)
We show ORDisCo achieves significant performance improvement on various semi-supervised learning benchmark datasets for SSCL.
arXiv Detail & Related papers (2021-01-02T09:04:14Z) - Out-distribution aware Self-training in an Open World Setting [62.19882458285749]
We leverage unlabeled data in an open world setting to further improve prediction performance.
We introduce out-distribution aware self-training, which includes a careful sample selection strategy.
Our classifiers are by design out-distribution aware and can thus distinguish task-related inputs from unrelated ones.
arXiv Detail & Related papers (2020-12-21T12:25:04Z) - Adversarial Knowledge Transfer from Unlabeled Data [62.97253639100014]
We present a novel Adversarial Knowledge Transfer framework for transferring knowledge from internet-scale unlabeled data to improve the performance of a classifier.
An important novel aspect of our method is that the unlabeled source data can be of different classes from those of the labeled target data, and there is no need to define a separate pretext task.
arXiv Detail & Related papers (2020-08-13T08:04:27Z) - Federated Self-Supervised Learning of Multi-Sensor Representations for
Embedded Intelligence [8.110949636804772]
Smartphones, wearables, and Internet of Things (IoT) devices produce a wealth of data that cannot be accumulated in a centralized repository for learning supervised models.
We propose a self-supervised approach termed textitscalogram-signal correspondence learning based on wavelet transform to learn useful representations from unlabeled sensor inputs.
We extensively assess the quality of learned features with our multi-view strategy on diverse public datasets, achieving strong performance in all domains.
arXiv Detail & Related papers (2020-07-25T21:59:17Z) - Omni-supervised Facial Expression Recognition via Distilled Data [120.11782405714234]
We propose omni-supervised learning to exploit reliable samples in a large amount of unlabeled data for network training.
We experimentally verify that the new dataset can significantly improve the ability of the learned FER model.
To tackle this, we propose to apply a dataset distillation strategy to compress the created dataset into several informative class-wise images.
arXiv Detail & Related papers (2020-05-18T09:36:51Z) - Multi-Center Federated Learning [62.57229809407692]
This paper proposes a novel multi-center aggregation mechanism for federated learning.
It learns multiple global models from the non-IID user data and simultaneously derives the optimal matching between users and centers.
Our experimental results on benchmark datasets show that our method outperforms several popular federated learning methods.
arXiv Detail & Related papers (2020-05-03T09:14:31Z) - Leveraging Semi-Supervised Learning for Fairness using Neural Networks [49.604038072384995]
There has been a growing concern about the fairness of decision-making systems based on machine learning.
In this paper, we propose a semi-supervised algorithm using neural networks benefiting from unlabeled data.
The proposed model, called SSFair, exploits the information in the unlabeled data to mitigate the bias in the training data.
arXiv Detail & Related papers (2019-12-31T09:11:26Z)
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.