An Efficient Imbalance-Aware Federated Learning Approach for Wearable
Healthcare with Autoregressive Ratio Observation
- URL: http://arxiv.org/abs/2310.14784v2
- Date: Mon, 30 Oct 2023 09:17:55 GMT
- Title: An Efficient Imbalance-Aware Federated Learning Approach for Wearable
Healthcare with Autoregressive Ratio Observation
- Authors: Wenhao Yan, He Li, Kaoru Ota, Mianxiong Dong
- Abstract summary: We propose a new federated learning framework FedImT, dedicated to addressing the challenges of class imbalance in federated learning scenarios.
FedImT contains an online scheme that can estimate the data composition during each round of aggregation.
Experiments demonstrate the effectiveness of FedImT in solving the imbalance problem without extra energy consumption and avoiding privacy risks.
- Score: 14.898997913387158
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Widely available healthcare services are now getting popular because of
advancements in wearable sensing techniques and mobile edge computing. People's
health information is collected by edge devices such as smartphones and
wearable bands for further analysis on servers, then send back suggestions and
alerts for abnormal conditions. The recent emergence of federated learning
allows users to train private data on local devices while updating models
collaboratively. However, the heterogeneous distribution of the health
condition data may lead to significant risks to model performance due to class
imbalance. Meanwhile, as FL training is powered by sharing gradients only with
the server, training data is almost inaccessible. The conventional solutions to
class imbalance do not work for federated learning. In this work, we propose a
new federated learning framework FedImT, dedicated to addressing the challenges
of class imbalance in federated learning scenarios. FedImT contains an online
scheme that can estimate the data composition during each round of aggregation,
then introduces a self-attenuating iterative equivalent to track variations of
multiple estimations and promptly tweak the balance of the loss computing for
minority classes. Experiments demonstrate the effectiveness of FedImT in
solving the imbalance problem without extra energy consumption and avoiding
privacy risks.
Related papers
- Addressing Class Variable Imbalance in Federated Semi-supervised
Learning [10.542178602467885]
We propose Federated Semi-supervised Learning for Class Variable Imbalance (FCVI) to solve class variable imbalance.
FCVI is used to mitigate the data imbalance due to changes of the number of classes.
Our scheme is proved to be significantly better than baseline methods, while maintaining client privacy.
arXiv Detail & Related papers (2023-03-21T12:50:17Z) - A Survey on Class Imbalance in Federated Learning [6.632451878730774]
Federated learning allows multiple client devices in a network to jointly train a machine learning model without direct exposure of clients' data.
It has been found that models trained with federated learning usually have worse performance than their counterparts trained in the standard centralized learning mode.
arXiv Detail & Related papers (2023-03-21T08:34:23Z) - Combating Exacerbated Heterogeneity for Robust Models in Federated
Learning [91.88122934924435]
Combination of adversarial training and federated learning can lead to the undesired robustness deterioration.
We propose a novel framework called Slack Federated Adversarial Training (SFAT)
We verify the rationality and effectiveness of SFAT on various benchmarked and real-world datasets.
arXiv Detail & Related papers (2023-03-01T06:16:15Z) - Federated Zero-Shot Learning for Visual Recognition [55.65879596326147]
We propose a novel Federated Zero-Shot Learning FedZSL framework.
FedZSL learns a central model from the decentralized data residing on edge devices.
The effectiveness and robustness of FedZSL are demonstrated by extensive experiments conducted on three zero-shot benchmark datasets.
arXiv Detail & Related papers (2022-09-05T14:49:34Z) - Density-Aware Personalized Training for Risk Prediction in Imbalanced
Medical Data [89.79617468457393]
Training models with imbalance rate (class density discrepancy) may lead to suboptimal prediction.
We propose a framework for training models for this imbalance issue.
We demonstrate our model's improved performance in real-world medical datasets.
arXiv Detail & Related papers (2022-07-23T00:39:53Z) - FedHiSyn: A Hierarchical Synchronous Federated Learning Framework for
Resource and Data Heterogeneity [56.82825745165945]
Federated Learning (FL) enables training a global model without sharing the decentralized raw data stored on multiple devices to protect data privacy.
We propose a hierarchical synchronous FL framework, i.e., FedHiSyn, to tackle the problems of straggler effects and outdated models.
We evaluate the proposed framework based on MNIST, EMNIST, CIFAR10 and CIFAR100 datasets and diverse heterogeneous settings of devices.
arXiv Detail & Related papers (2022-06-21T17:23:06Z) - Concept drift detection and adaptation for federated and continual
learning [55.41644538483948]
Smart devices can collect vast amounts of data from their environment.
This data is suitable for training machine learning models, which can significantly improve their behavior.
In this work, we present a new method, called Concept-Drift-Aware Federated Averaging.
arXiv Detail & Related papers (2021-05-27T17:01:58Z) - Federated learning with class imbalance reduction [24.044750119251308]
Federated learning (FL) is a technique that enables a large amount of edge computing devices to collaboratively train a global learning model.
Due to privacy concerns, the raw data on devices could not be available for centralized server.
In this paper, an estimation scheme is designed to reveal the class distribution without the awareness of raw data.
arXiv Detail & Related papers (2020-11-23T08:13:43Z) - Fed-Focal Loss for imbalanced data classification in Federated Learning [2.2172881631608456]
Federated Learning has a central server coordinating the training of a model on a network of devices.
One of the challenges is variable training performance when the dataset has a class imbalance.
We propose to address the class imbalance by reshaping cross-entropy loss such that it down-weights the loss assigned to well-classified examples along the lines of focal loss.
arXiv Detail & Related papers (2020-11-12T09:52:14Z) - Addressing Class Imbalance in Federated Learning [10.970632986559547]
Federated learning (FL) is a promising approach for training decentralized data located on local client devices.
We propose a monitoring scheme that can infer the composition of training data for each FL round, and design a new loss function -- textbfRatio Loss to mitigate the impact.
arXiv Detail & Related papers (2020-08-14T07:28:08Z) - WAFFLe: Weight Anonymized Factorization for Federated Learning [88.44939168851721]
In domains where data are sensitive or private, there is great value in methods that can learn in a distributed manner without the data ever leaving the local devices.
We propose Weight Anonymized Factorization for Federated Learning (WAFFLe), an approach that combines the Indian Buffet Process with a shared dictionary of weight factors for neural networks.
arXiv Detail & Related papers (2020-08-13T04:26:31Z)
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.